From c358c4a9bb3290ae98c205cf208658ff1aed9ee5 Mon Sep 17 00:00:00 2001 From: rebreda Date: Wed, 4 Mar 2026 21:07:10 -0500 Subject: [PATCH 01/11] initial finetune pipeline --- pyproject.toml | 9 + src/listenr/config_manager.py | 21 +++ src/listenr/constants.py | 39 ++++ src/listenr/finetune/__init__.py | 14 ++ src/listenr/finetune/data.py | 175 ++++++++++++++++++ src/listenr/finetune/metrics.py | 67 +++++++ src/listenr/finetune/model.py | 158 ++++++++++++++++ src/listenr/finetune/train.py | 306 +++++++++++++++++++++++++++++++ tests/test_finetune_data.py | 183 ++++++++++++++++++ tests/test_finetune_metrics.py | 151 +++++++++++++++ tests/test_finetune_model.py | 173 +++++++++++++++++ 11 files changed, 1296 insertions(+) create mode 100644 src/listenr/finetune/__init__.py create mode 100644 src/listenr/finetune/data.py create mode 100644 src/listenr/finetune/metrics.py create mode 100644 src/listenr/finetune/model.py create mode 100644 src/listenr/finetune/train.py create mode 100644 tests/test_finetune_data.py create mode 100644 tests/test_finetune_metrics.py create mode 100644 tests/test_finetune_model.py diff --git a/pyproject.toml b/pyproject.toml index 2521741..fc0ce3c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,10 +18,19 @@ dependencies = [ dev = [ "pytest", ] +finetune = [ + "transformers>=4.40", + "peft>=0.11", + "accelerate>=0.29", + "datasets", + "evaluate", + "jiwer", +] [project.scripts] listenr = "listenr.cli:main" listenr-build-dataset = "listenr.build_dataset:main" +listenr-finetune = "listenr.finetune.train:main" [tool.setuptools.packages.find] where = ["src"] diff --git a/src/listenr/config_manager.py b/src/listenr/config_manager.py index 8738fb0..9903e4c 100644 --- a/src/listenr/config_manager.py +++ b/src/listenr/config_manager.py @@ -62,6 +62,27 @@ 'seed': '42', # Random seed for reproducible splits 'format': 'csv', # Output format: csv, hf, or both }, + 'Finetune': { + 'base_model': 'openai/whisper-small', # HuggingFace model id to fine-tune + 'language': 'english', # Target language for the processor/tokenizer + 'task': 'transcribe', # 'transcribe' or 'translate' + 'lora_r': '8', # LoRA rank + 'lora_alpha': '32', # LoRA scaling factor + 'lora_dropout': '0.1', # LoRA dropout + 'lora_target_modules': 'q_proj,v_proj', # Comma-separated decoder attention projections + 'freeze_encoder': 'true', # Freeze Whisper encoder weights during training + 'learning_rate': '1e-4', # AdamW learning rate + 'warmup_steps': '100', + 'max_steps': '2000', + 'batch_size': '8', # Per-device train batch size + 'grad_accum_steps': '2', # Gradient accumulation steps + 'fp16': 'false', # Mixed precision fp16 (CUDA; use bf16 for AMD ROCm) + 'bf16': 'false', # Mixed precision bf16 (recommended for AMD ROCm RDNA2+) + 'output_dir': '~/listenr_finetune', # Where adapter checkpoints are saved + 'eval_steps': '200', # Evaluate every N training steps + 'save_steps': '400', # Save checkpoint every N training steps + 'generation_max_length': '128', # Max decode length during evaluation + }, 'Output': { 'file': '~/transcripts_raw.txt', 'llm_file': '~/transcripts_clean.txt', diff --git a/src/listenr/constants.py b/src/listenr/constants.py index dbddef6..d47b884 100644 --- a/src/listenr/constants.py +++ b/src/listenr/constants.py @@ -119,6 +119,45 @@ DATASET_FORMAT: str = _raw_dataset_format DATASET_STRIP_TAGS: bool = cfg.get_bool_setting("Dataset", "strip_tags", True) +# --------------------------------------------------------------------------- +# Finetune +# --------------------------------------------------------------------------- + +FINETUNE_BASE_MODEL: str = ( + cfg.get_setting("Finetune", "base_model", "openai/whisper-small") + or "openai/whisper-small" +) +FINETUNE_LANGUAGE: str = ( + cfg.get_setting("Finetune", "language", "english") or "english" +) +FINETUNE_TASK: str = ( + cfg.get_setting("Finetune", "task", "transcribe") or "transcribe" +) +FINETUNE_LORA_R: int = cfg.get_int_setting("Finetune", "lora_r", 8) +FINETUNE_LORA_ALPHA: int = cfg.get_int_setting("Finetune", "lora_alpha", 32) +FINETUNE_LORA_DROPOUT: float = cfg.get_float_setting("Finetune", "lora_dropout", 0.1) +FINETUNE_LORA_TARGET_MODULES: list[str] = ( + cfg.get_setting("Finetune", "lora_target_modules", "q_proj,v_proj") + or "q_proj,v_proj" +).split(",") +FINETUNE_FREEZE_ENCODER: bool = cfg.get_bool_setting("Finetune", "freeze_encoder", True) +FINETUNE_LEARNING_RATE: float = cfg.get_float_setting("Finetune", "learning_rate", 1e-4) +FINETUNE_WARMUP_STEPS: int = cfg.get_int_setting("Finetune", "warmup_steps", 100) +FINETUNE_MAX_STEPS: int = cfg.get_int_setting("Finetune", "max_steps", 2000) +FINETUNE_BATCH_SIZE: int = cfg.get_int_setting("Finetune", "batch_size", 8) +FINETUNE_GRAD_ACCUM_STEPS: int = cfg.get_int_setting("Finetune", "grad_accum_steps", 2) +FINETUNE_FP16: bool = cfg.get_bool_setting("Finetune", "fp16", False) +FINETUNE_BF16: bool = cfg.get_bool_setting("Finetune", "bf16", False) +FINETUNE_OUTPUT_DIR: Path = Path( + cfg.get_setting("Finetune", "output_dir", "~/listenr_finetune") + or "~/listenr_finetune" +).expanduser() +FINETUNE_EVAL_STEPS: int = cfg.get_int_setting("Finetune", "eval_steps", 200) +FINETUNE_SAVE_STEPS: int = cfg.get_int_setting("Finetune", "save_steps", 400) +FINETUNE_GENERATION_MAX_LENGTH: int = cfg.get_int_setting( + "Finetune", "generation_max_length", 128 +) + # --------------------------------------------------------------------------- # Output / transcript files # --------------------------------------------------------------------------- diff --git a/src/listenr/finetune/__init__.py b/src/listenr/finetune/__init__.py new file mode 100644 index 0000000..e85c735 --- /dev/null +++ b/src/listenr/finetune/__init__.py @@ -0,0 +1,14 @@ +""" +listenr.finetune — LoRA fine-tuning pipeline for Whisper ASR. + +Public API (all heavy imports are deferred until actually called so that the +package can be imported without the optional fine-tuning dependencies installed): + + from listenr.finetune.data import make_processor, make_dataset, WhisperDataCollator + from listenr.finetune.model import load_base_model, make_lora_config, apply_lora, freeze_encoder + from listenr.finetune.metrics import make_compute_metrics + +Entry point:: + + listenr-finetune [options] +""" diff --git a/src/listenr/finetune/data.py b/src/listenr/finetune/data.py new file mode 100644 index 0000000..6fc028e --- /dev/null +++ b/src/listenr/finetune/data.py @@ -0,0 +1,175 @@ +""" +data.py — Dataset preparation for Whisper LoRA fine-tuning. + +All functions are pure (no global state, no side effects) to keep them +easy to test and reuse. + +Requires the ``finetune`` optional dependencies:: + + uv pip install "listenr[finetune]" + +Public API +---------- +make_processor(model_id, language, task) -> WhisperProcessor +prepare_example(batch, processor) -> dict with input_features + labels +make_dataset(hf_dataset_path, processor) -> DatasetDict with train/dev/test +WhisperDataCollator -> dataclass, handles per-batch padding +""" + +from __future__ import annotations + +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from transformers import WhisperProcessor # noqa: F401 + + +# --------------------------------------------------------------------------- +# Processor +# --------------------------------------------------------------------------- + +def make_processor(model_id: str, language: str, task: str) -> "WhisperProcessor": + """Load the WhisperProcessor (feature extractor + tokenizer) from *model_id*. + + Parameters + ---------- + model_id: + HuggingFace Hub identifier, e.g. ``"openai/whisper-small"``. + language: + Target language, e.g. ``"english"``. Passed to the tokenizer so the + correct language token is prepended during encoding. + task: + ``"transcribe"`` (default) or ``"translate"``. + """ + try: + from transformers import WhisperProcessor as _WhisperProcessor + except ImportError: + print( + "ERROR: transformers is required. Install with:\n" + " uv pip install \"listenr[finetune]\"", + file=sys.stderr, + ) + sys.exit(1) + + return _WhisperProcessor.from_pretrained(model_id, language=language, task=task) + + +# --------------------------------------------------------------------------- +# Feature preparation +# --------------------------------------------------------------------------- + +def prepare_example(batch: dict, processor: Any) -> dict: + """Convert a single dataset example into model-ready tensors. + + Expects the dataset to have been created by ``listenr-build-dataset --format hf`` + (or ``both``), so the ``audio_path`` column is a decoded HuggingFace + :class:`datasets.Audio` dict with keys ``array`` and ``sampling_rate``. + + Returns a dict with: + + ``input_features`` + Log-Mel spectrogram, shape ``(80, 3000)``, as produced by the Whisper + feature extractor. + ``labels`` + Token ids for ``corrected_transcription``. + """ + audio = batch["audio_path"] # decoded Audio dict: {"array": ndarray, "sampling_rate": int} + input_features = processor.feature_extractor( + audio["array"], + sampling_rate=audio["sampling_rate"], + ).input_features[0] + + labels = processor.tokenizer(batch["corrected_transcription"]).input_ids + + return {"input_features": input_features, "labels": labels} + + +# --------------------------------------------------------------------------- +# Dataset loader +# --------------------------------------------------------------------------- + +def make_dataset(hf_dataset_path: Path, processor: Any) -> Any: + """Load the on-disk HuggingFace DatasetDict and apply feature preparation. + + *hf_dataset_path* should be the ``hf_dataset/`` subdirectory written by + ``listenr-build-dataset --format hf``. + + The returned DatasetDict has columns ``input_features`` and ``labels`` + ready for the Trainer. + """ + try: + from datasets import load_from_disk + except ImportError: + print( + "ERROR: datasets is required. Install with:\n" + " uv pip install \"listenr[finetune]\"", + file=sys.stderr, + ) + sys.exit(1) + + dataset = load_from_disk(str(hf_dataset_path)) + + # Map feature prep; remove raw columns afterwards to save memory. + raw_columns = dataset.column_names + # column_names is a dict for DatasetDict + cols_to_remove = list(next(iter(raw_columns.values())) if isinstance(raw_columns, dict) else raw_columns) + + dataset = dataset.map( + lambda batch: prepare_example(batch, processor), + remove_columns=cols_to_remove, + ) + return dataset + + +# --------------------------------------------------------------------------- +# Data collator +# --------------------------------------------------------------------------- + +@dataclass +class WhisperDataCollator: + """Collate a list of ``prepare_example`` outputs into a padded batch. + + Handles the two-part padding requirement of Whisper: + + * ``input_features`` — already fixed-size (80 × 3000); just stack into + a tensor. + * ``labels`` — variable-length; pad to the longest sequence in the batch + and replace padding positions with ``-100`` so they are ignored by the + cross-entropy loss. The BOS token is trimmed if present (Whisper's + Trainer appends it automatically). + """ + + processor: Any + decoder_start_token_id: int + + def __call__(self, features: list[dict]) -> dict: + try: + import torch + except ImportError: + print( + "ERROR: torch is required. Install with:\n" + " uv pip install \"listenr[finetune]\"", + file=sys.stderr, + ) + sys.exit(1) + + # --- input features (fixed size → just convert to tensors) --- + input_batch = [{"input_features": f["input_features"]} for f in features] + batch = self.processor.feature_extractor.pad(input_batch, return_tensors="pt") + + # --- labels (variable length → pad + mask) --- + label_batch = [{"input_ids": f["labels"]} for f in features] + labels_padded = self.processor.tokenizer.pad(label_batch, return_tensors="pt") + labels = labels_padded.input_ids.masked_fill( + labels_padded.attention_mask.ne(1), -100 + ) + + # Trim leading BOS token if the tokenizer prepended it; Trainer adds it back. + if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item(): + labels = labels[:, 1:] + + batch["labels"] = labels + return batch diff --git a/src/listenr/finetune/metrics.py b/src/listenr/finetune/metrics.py new file mode 100644 index 0000000..d602123 --- /dev/null +++ b/src/listenr/finetune/metrics.py @@ -0,0 +1,67 @@ +""" +metrics.py — Evaluation metrics for Whisper fine-tuning. + +Provides a factory that returns a ``compute_metrics`` closure compatible with +:class:`transformers.Seq2SeqTrainer`. + +Requires the ``finetune`` optional dependencies:: + + uv pip install "listenr[finetune]" + +Public API +---------- +make_compute_metrics(tokenizer) -> Callable[[EvalPrediction], dict] +""" + +from __future__ import annotations + +import sys +from typing import Any, Callable + + +def make_compute_metrics(tokenizer: Any) -> Callable: + """Return a ``compute_metrics`` function for use with ``Seq2SeqTrainer``. + + The returned function computes Word Error Rate (WER) between the model's + predicted token ids and the ground-truth label ids. + + Parameters + ---------- + tokenizer: + A ``WhisperTokenizer`` (or ``WhisperProcessor``) used to decode ids + back to text strings. + + Example + ------- + :: + + compute_metrics = make_compute_metrics(processor.tokenizer) + trainer = Seq2SeqTrainer(..., compute_metrics=compute_metrics) + """ + try: + import evaluate + except ImportError: + print( + "ERROR: evaluate is required. Install with:\n" + " uv pip install \"listenr[finetune]\"", + file=sys.stderr, + ) + sys.exit(1) + + wer_metric = evaluate.load("wer") + + def compute_metrics(pred: Any) -> dict[str, float]: + pred_ids = pred.predictions + label_ids = pred.label_ids + + # Replace -100 (padding / ignored tokens) with the pad token id so the + # tokenizer can decode them without errors. + label_ids[label_ids == -100] = tokenizer.pad_token_id + + pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True) + label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True) + + wer = 100.0 * wer_metric.compute(predictions=pred_str, references=label_str) + return {"wer": wer} + + return compute_metrics diff --git a/src/listenr/finetune/model.py b/src/listenr/finetune/model.py new file mode 100644 index 0000000..78f4ddf --- /dev/null +++ b/src/listenr/finetune/model.py @@ -0,0 +1,158 @@ +""" +model.py — Model loading and LoRA configuration for Whisper fine-tuning. + +All functions are pure (no global state) to make them easy to test in +isolation. + +Requires the ``finetune`` optional dependencies:: + + uv pip install "listenr[finetune]" + +Public API +---------- +load_base_model(model_id) -> WhisperForConditionalGeneration +make_lora_config(r, alpha, dropout, modules) -> LoraConfig +apply_lora(model, lora_config) -> PeftModel +freeze_encoder(model) -> None (in-place) +count_trainable_params(model) -> (trainable, total) +""" + +from __future__ import annotations + +import sys +from typing import Any + + +# --------------------------------------------------------------------------- +# Model loading +# --------------------------------------------------------------------------- + +def load_base_model(model_id: str) -> Any: + """Load the pre-trained Whisper model from *model_id*. + + The model is returned in full precision (fp32). The caller is responsible + for casting to fp16/bf16 via ``Seq2SeqTrainingArguments`` at training time. + """ + try: + from transformers import WhisperForConditionalGeneration + except ImportError: + print( + "ERROR: transformers is required. Install with:\n" + " uv pip install \"listenr[finetune]\"", + file=sys.stderr, + ) + sys.exit(1) + + return WhisperForConditionalGeneration.from_pretrained(model_id) + + +# --------------------------------------------------------------------------- +# LoRA configuration +# --------------------------------------------------------------------------- + +def make_lora_config( + r: int, + alpha: int, + dropout: float, + target_modules: list[str], +) -> Any: + """Build a PEFT :class:`LoraConfig` for a Whisper Seq2Seq model. + + Parameters + ---------- + r: + LoRA rank. Higher values → more expressiveness but more parameters. + ``8`` is a good default for domain adaptation. + alpha: + LoRA scaling factor. Effective scale = ``alpha / r``; keeping + ``alpha = 4 * r`` is a common heuristic. + dropout: + Dropout applied to the LoRA layers during training. + target_modules: + Which attention projections to adapt. Whisper decoder attention has + ``q_proj``, ``k_proj``, ``v_proj``, ``out_proj``; adapting just + ``q_proj`` and ``v_proj`` is the minimal effective set. + """ + try: + from peft import LoraConfig, TaskType + except ImportError: + print( + "ERROR: peft is required. Install with:\n" + " uv pip install \"listenr[finetune]\"", + file=sys.stderr, + ) + sys.exit(1) + + return LoraConfig( + task_type=TaskType.SEQ_2_SEQ_LM, + inference_mode=False, + r=r, + lora_alpha=alpha, + lora_dropout=dropout, + target_modules=target_modules, + ) + + +# --------------------------------------------------------------------------- +# Apply LoRA +# --------------------------------------------------------------------------- + +def apply_lora(model: Any, lora_config: Any) -> Any: + """Wrap *model* with PEFT LoRA adapters defined by *lora_config*. + + Returns the wrapped :class:`peft.PeftModel`. Only the LoRA adapter + parameters will have ``requires_grad=True``; all other weights are frozen + by PEFT automatically. + """ + try: + from peft import get_peft_model + except ImportError: + print( + "ERROR: peft is required. Install with:\n" + " uv pip install \"listenr[finetune]\"", + file=sys.stderr, + ) + sys.exit(1) + + return get_peft_model(model, lora_config) + + +# --------------------------------------------------------------------------- +# Encoder freezing +# --------------------------------------------------------------------------- + +def freeze_encoder(model: Any) -> None: + """Freeze all parameters in the Whisper encoder (in-place). + + The encoder already extracts high-quality audio features from pre-training; + only the decoder needs domain adaptation for a new vocabulary/style. + + Works both on a raw ``WhisperForConditionalGeneration`` and on a + ``PeftModel`` wrapping one. + """ + # PeftModel wraps the base model under .base_model.model; raw Whisper has + # .model.encoder directly. + try: + encoder = model.base_model.model.model.encoder + except AttributeError: + encoder = model.model.encoder + + for param in encoder.parameters(): + param.requires_grad = False + + +# --------------------------------------------------------------------------- +# Parameter counting +# --------------------------------------------------------------------------- + +def count_trainable_params(model: Any) -> tuple[int, int]: + """Return ``(trainable_params, total_params)`` for *model*. + + Useful for logging:: + + trainable, total = count_trainable_params(model) + print(f"Training {trainable:,} / {total:,} params ({100*trainable/total:.2f}%)") + """ + trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) + total = sum(p.numel() for p in model.parameters()) + return trainable, total diff --git a/src/listenr/finetune/train.py b/src/listenr/finetune/train.py new file mode 100644 index 0000000..c798b16 --- /dev/null +++ b/src/listenr/finetune/train.py @@ -0,0 +1,306 @@ +#!/usr/bin/env python3 +""" +train.py — CLI entry point for Whisper LoRA fine-tuning. + +Loads a HuggingFace DatasetDict produced by ``listenr-build-dataset --format hf``, +prepares features, wraps the base Whisper model with LoRA adapters, and runs +``Seq2SeqTrainer``. Only the adapter weights are saved — the base model is not +copied. + +Usage: + listenr-finetune [options] + +Examples: + # Fine-tune on your listenr dataset with all defaults from config + listenr-finetune + + # Custom dataset location and output dir + listenr-finetune --dataset ~/listenr_dataset/hf_dataset --output ~/my_adapter + + # Quick smoke-test: load data + model, print stats, then exit + listenr-finetune --dry-run + + # Override training budget + listenr-finetune --max-steps 500 --eval-steps 100 --save-steps 200 + + # AMD ROCm GPU: use bf16 instead of fp16 + listenr-finetune --bf16 +""" + +import argparse +import logging +import sys +from pathlib import Path + +from listenr.constants import ( + DATASET_OUTPUT, + FINETUNE_BASE_MODEL, + FINETUNE_BATCH_SIZE, + FINETUNE_BF16, + FINETUNE_EVAL_STEPS, + FINETUNE_FP16, + FINETUNE_FREEZE_ENCODER, + FINETUNE_GENERATION_MAX_LENGTH, + FINETUNE_GRAD_ACCUM_STEPS, + FINETUNE_LANGUAGE, + FINETUNE_LEARNING_RATE, + FINETUNE_LORA_ALPHA, + FINETUNE_LORA_DROPOUT, + FINETUNE_LORA_R, + FINETUNE_LORA_TARGET_MODULES, + FINETUNE_MAX_STEPS, + FINETUNE_OUTPUT_DIR, + FINETUNE_SAVE_STEPS, + FINETUNE_TASK, + FINETUNE_WARMUP_STEPS, +) + +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") +logger = logging.getLogger("listenr.finetune.train") + + +def _resolve_dataset_path(dataset_arg: Path | None, dataset_output: Path) -> Path: + """Return the hf_dataset directory to load from. + + If *dataset_arg* is given explicitly, use it. Otherwise fall back to + ``/hf_dataset`` (the default location written by + ``listenr-build-dataset --format hf``). + """ + if dataset_arg is not None: + return Path(dataset_arg).expanduser() + return dataset_output / "hf_dataset" + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Fine-tune Whisper with LoRA adapters on your listenr dataset." + ) + + # --- data --- + parser.add_argument( + "--dataset", + type=Path, + default=None, + metavar="DIR", + help=( + "Path to the hf_dataset directory written by listenr-build-dataset " + f"(default: {DATASET_OUTPUT / 'hf_dataset'})" + ), + ) + parser.add_argument( + "--output", + type=Path, + default=FINETUNE_OUTPUT_DIR, + metavar="DIR", + help=f"Where to save the LoRA adapter checkpoint (default: {FINETUNE_OUTPUT_DIR})", + ) + + # --- model --- + parser.add_argument( + "--base-model", + default=FINETUNE_BASE_MODEL, + metavar="MODEL_ID", + help=f"HuggingFace model id to fine-tune (default: {FINETUNE_BASE_MODEL})", + ) + parser.add_argument( + "--language", + default=FINETUNE_LANGUAGE, + help=f"Target language for the processor (default: {FINETUNE_LANGUAGE})", + ) + parser.add_argument( + "--task", + default=FINETUNE_TASK, + choices=["transcribe", "translate"], + help=f"Task token prepended during tokenisation (default: {FINETUNE_TASK})", + ) + parser.add_argument( + "--no-freeze-encoder", + dest="freeze_encoder", + action="store_false", + default=FINETUNE_FREEZE_ENCODER, + help="Train the encoder too (default: freeze it)", + ) + + # --- LoRA --- + parser.add_argument("--lora-r", type=int, default=FINETUNE_LORA_R, + help=f"LoRA rank (default: {FINETUNE_LORA_R})") + parser.add_argument("--lora-alpha", type=int, default=FINETUNE_LORA_ALPHA, + help=f"LoRA scaling factor (default: {FINETUNE_LORA_ALPHA})") + parser.add_argument("--lora-dropout", type=float, default=FINETUNE_LORA_DROPOUT, + help=f"LoRA dropout (default: {FINETUNE_LORA_DROPOUT})") + + # --- training --- + parser.add_argument("--max-steps", type=int, default=FINETUNE_MAX_STEPS, + help=f"Total training steps (default: {FINETUNE_MAX_STEPS})") + parser.add_argument("--batch-size", type=int, default=FINETUNE_BATCH_SIZE, + help=f"Per-device train batch size (default: {FINETUNE_BATCH_SIZE})") + parser.add_argument("--grad-accum", type=int, default=FINETUNE_GRAD_ACCUM_STEPS, + help=f"Gradient accumulation steps (default: {FINETUNE_GRAD_ACCUM_STEPS})") + parser.add_argument("--learning-rate", type=float, default=FINETUNE_LEARNING_RATE, + help=f"Learning rate (default: {FINETUNE_LEARNING_RATE})") + parser.add_argument("--warmup-steps", type=int, default=FINETUNE_WARMUP_STEPS, + help=f"LR warmup steps (default: {FINETUNE_WARMUP_STEPS})") + parser.add_argument("--eval-steps", type=int, default=FINETUNE_EVAL_STEPS, + help=f"Evaluate every N steps (default: {FINETUNE_EVAL_STEPS})") + parser.add_argument("--save-steps", type=int, default=FINETUNE_SAVE_STEPS, + help=f"Save checkpoint every N steps (default: {FINETUNE_SAVE_STEPS})") + parser.add_argument( + "--fp16", + action="store_true", + default=FINETUNE_FP16, + help="Enable fp16 mixed precision (CUDA GPUs; not recommended for AMD ROCm)", + ) + parser.add_argument( + "--bf16", + action="store_true", + default=FINETUNE_BF16, + help="Enable bf16 mixed precision (recommended for AMD ROCm RDNA2+)", + ) + + # --- misc --- + parser.add_argument( + "--dry-run", + action="store_true", + help="Load data + model, print stats, then exit without training", + ) + + args = parser.parse_args() + + # Deferred imports — only required when actually running, so the package is + # importable without the finetune extras installed. + try: + from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments + except ImportError: + print( + "ERROR: transformers is required. Install with:\n" + " uv pip install \"listenr[finetune]\"", + file=sys.stderr, + ) + sys.exit(1) + + from listenr.finetune.data import make_processor, make_dataset, WhisperDataCollator + from listenr.finetune.model import ( + load_base_model, + make_lora_config, + apply_lora, + freeze_encoder, + count_trainable_params, + ) + from listenr.finetune.metrics import make_compute_metrics + + # ----------------------------------------------------------------------- + # 1. Dataset + # ----------------------------------------------------------------------- + dataset_path = _resolve_dataset_path(args.dataset, DATASET_OUTPUT) + if not dataset_path.exists(): + logger.error( + f"Dataset not found at {dataset_path}.\n" + "Run: listenr-build-dataset --format hf" + ) + sys.exit(1) + + logger.info(f"Loading dataset from {dataset_path}") + processor = make_processor(args.base_model, args.language, args.task) + dataset = make_dataset(dataset_path, processor) + logger.info(f"Dataset splits: { {k: len(v) for k, v in dataset.items()} }") + + # ----------------------------------------------------------------------- + # 2. Model + LoRA + # ----------------------------------------------------------------------- + logger.info(f"Loading base model: {args.base_model}") + model = load_base_model(args.base_model) + + # Set generation config to avoid deprecation warnings and force correct tokens. + model.generation_config.language = args.language + model.generation_config.task = args.task + model.generation_config.forced_decoder_ids = None + + lora_cfg = make_lora_config( + r=args.lora_r, + alpha=args.lora_alpha, + dropout=args.lora_dropout, + target_modules=FINETUNE_LORA_TARGET_MODULES, + ) + model = apply_lora(model, lora_cfg) + + if args.freeze_encoder: + freeze_encoder(model) + logger.info("Encoder frozen.") + + trainable, total = count_trainable_params(model) + logger.info( + f"Trainable params: {trainable:,} / {total:,} " + f"({100 * trainable / total:.2f}%)" + ) + + if args.dry_run: + logger.info("Dry run — exiting without training.") + return + + # ----------------------------------------------------------------------- + # 3. Training + # ----------------------------------------------------------------------- + output_dir = Path(args.output).expanduser() + output_dir.mkdir(parents=True, exist_ok=True) + + data_collator = WhisperDataCollator( + processor=processor, + decoder_start_token_id=model.config.decoder_start_token_id, + ) + compute_metrics = make_compute_metrics(processor.tokenizer) + + training_args = Seq2SeqTrainingArguments( + output_dir=str(output_dir), + per_device_train_batch_size=args.batch_size, + gradient_accumulation_steps=args.grad_accum, + learning_rate=args.learning_rate, + warmup_steps=args.warmup_steps, + max_steps=args.max_steps, + gradient_checkpointing=True, + fp16=args.fp16, + bf16=args.bf16, + eval_strategy="steps", + per_device_eval_batch_size=max(1, args.batch_size // 2), + predict_with_generate=True, + generation_max_length=FINETUNE_GENERATION_MAX_LENGTH, + save_steps=args.save_steps, + eval_steps=args.eval_steps, + logging_steps=max(1, args.eval_steps // 4), + report_to=["tensorboard"], + load_best_model_at_end=True, + metric_for_best_model="wer", + greater_is_better=False, + remove_unused_columns=False, # keep audio columns until collator runs + ) + + train_split = dataset.get("train") + eval_split = dataset.get("dev") or dataset.get("test") + + if train_split is None: + logger.error("Dataset has no 'train' split.") + sys.exit(1) + if eval_split is None: + logger.warning("No 'dev' or 'test' split found — evaluation will be skipped.") + + trainer = Seq2SeqTrainer( + args=training_args, + model=model, + train_dataset=train_split, + eval_dataset=eval_split, + data_collator=data_collator, + compute_metrics=compute_metrics, + processing_class=processor.feature_extractor, + ) + + logger.info("Starting training…") + trainer.train() + + # Save only the adapter weights. + logger.info(f"Saving LoRA adapter to {output_dir}") + model.save_pretrained(str(output_dir)) + processor.save_pretrained(str(output_dir)) + logger.info("Done.") + + +if __name__ == "__main__": + main() diff --git a/tests/test_finetune_data.py b/tests/test_finetune_data.py new file mode 100644 index 0000000..1cedf08 --- /dev/null +++ b/tests/test_finetune_data.py @@ -0,0 +1,183 @@ +""" +test_finetune_data.py — Unit tests for listenr.finetune.data + +Tests cover the pure data-preparation logic (prepare_example, WhisperDataCollator) +using lightweight mocks so no GPU, network access, or optional dependencies are +required to run them. +""" + +from __future__ import annotations + +from unittest.mock import MagicMock + +import numpy as np +import pytest + +from listenr.finetune.data import WhisperDataCollator, prepare_example + + +# --------------------------------------------------------------------------- +# Helpers — lightweight mock processor +# --------------------------------------------------------------------------- + +def _make_processor(pad_token_id: int = 50256) -> MagicMock: + """Return a minimal mock WhisperProcessor sufficient for these tests.""" + processor = MagicMock() + + # feature_extractor.pad returns a dict-like with "input_features" tensor + def _fe_pad(features, return_tensors): + import torch + stacked = torch.stack([torch.tensor(f["input_features"]) for f in features]) + return {"input_features": stacked} + + processor.feature_extractor.pad.side_effect = _fe_pad + + # tokenizer.pad returns padded input_ids + attention_mask + def _tok_pad(label_batch, return_tensors): + import torch + seqs = [f["input_ids"] for f in label_batch] + max_len = max(len(s) for s in seqs) + padded = [s + [pad_token_id] * (max_len - len(s)) for s in seqs] + mask = [[1] * len(s) + [0] * (max_len - len(s)) for s in seqs] + return MagicMock( + input_ids=torch.tensor(padded), + attention_mask=torch.tensor(mask), + ) + + processor.tokenizer.pad.side_effect = _tok_pad + processor.tokenizer.pad_token_id = pad_token_id + return processor + + +def _make_audio_dict(duration_s: float = 0.5, sample_rate: int = 16000) -> dict: + """Return a decoded HF Audio dict with a sine-wave array.""" + n = int(duration_s * sample_rate) + t = np.linspace(0, duration_s, n, endpoint=False) + array = (np.sin(2 * np.pi * 440 * t)).astype(np.float32) + return {"array": array, "sampling_rate": sample_rate} + + +# --------------------------------------------------------------------------- +# prepare_example +# --------------------------------------------------------------------------- + +class TestPrepareExample: + def test_returns_input_features_and_labels(self): + audio = _make_audio_dict() + processor = MagicMock() + processor.feature_extractor.return_value.input_features = [np.zeros((80, 3000), dtype=np.float32)] + processor.tokenizer.return_value.input_ids = [1, 2, 3, 4] + + result = prepare_example( + {"audio_path": audio, "corrected_transcription": "hello world"}, + processor, + ) + + assert "input_features" in result + assert "labels" in result + + def test_feature_extractor_called_with_correct_sample_rate(self): + audio = _make_audio_dict(sample_rate=16000) + processor = MagicMock() + processor.feature_extractor.return_value.input_features = [np.zeros((80, 3000))] + processor.tokenizer.return_value.input_ids = [1] + + prepare_example( + {"audio_path": audio, "corrected_transcription": "hi"}, + processor, + ) + + call_kwargs = processor.feature_extractor.call_args + assert call_kwargs.kwargs["sampling_rate"] == 16000 + + def test_tokenizer_called_with_corrected_transcription(self): + audio = _make_audio_dict() + processor = MagicMock() + processor.feature_extractor.return_value.input_features = [np.zeros((80, 3000))] + processor.tokenizer.return_value.input_ids = [5, 6] + + prepare_example( + {"audio_path": audio, "corrected_transcription": "test sentence"}, + processor, + ) + + processor.tokenizer.assert_called_once_with("test sentence") + + def test_input_features_is_first_element(self): + """prepare_example takes .input_features[0] — the first example in the batch.""" + expected = np.ones((80, 3000), dtype=np.float32) + audio = _make_audio_dict() + processor = MagicMock() + processor.feature_extractor.return_value.input_features = [expected, np.zeros((80, 3000))] + processor.tokenizer.return_value.input_ids = [1] + + result = prepare_example({"audio_path": audio, "corrected_transcription": "x"}, processor) + np.testing.assert_array_equal(result["input_features"], expected) + + +# --------------------------------------------------------------------------- +# WhisperDataCollator +# --------------------------------------------------------------------------- + +class TestWhisperDataCollator: + """Tests rely on actual torch tensors via the mock _fe_pad / _tok_pad helpers.""" + + @pytest.fixture() + def collator(self): + processor = _make_processor(pad_token_id=50256) + return WhisperDataCollator(processor=processor, decoder_start_token_id=50258) + + def _make_features(self, label_lengths: list[int]) -> list[dict]: + """Return fake prepared examples with varying label lengths.""" + return [ + { + "input_features": np.zeros((80, 3000), dtype=np.float32), + "labels": list(range(1, length + 1)), + } + for length in label_lengths + ] + + def test_batch_has_input_features_and_labels(self, collator): + batch = collator(self._make_features([3, 5])) + assert "input_features" in batch + assert "labels" in batch + + def test_input_features_shape(self, collator): + import torch + features = self._make_features([3, 4]) + batch = collator(features) + assert batch["input_features"].shape == torch.Size([2, 80, 3000]) + + def test_labels_padded_to_max_length(self, collator): + batch = collator(self._make_features([3, 6])) + # All label rows should have the same length after padding + assert batch["labels"].shape[1] == 6 + + def test_padding_positions_replaced_with_minus100(self, collator): + """Shorter labels should have -100 in the padded positions.""" + batch = collator(self._make_features([2, 5])) + short_row = batch["labels"][0] # length 2, padded to 5 + assert (short_row[2:] == -100).all(), \ + f"Expected padding -100 from index 2, got {short_row}" + + def test_bos_trimmed_when_all_rows_start_with_decoder_start_token(self, collator): + """If every label sequence starts with the BOS token, it should be removed.""" + bos = 50258 + features = [ + {"input_features": np.zeros((80, 3000)), "labels": [bos, 1, 2]}, + {"input_features": np.zeros((80, 3000)), "labels": [bos, 3, 4, 5]}, + ] + batch = collator(features) + # After BOS removal both rows should NOT start with 50258 + assert batch["labels"][0, 0].item() != bos + assert batch["labels"][1, 0].item() != bos + + def test_bos_not_trimmed_when_mixed_start_tokens(self, collator): + """BOS trim only happens when ALL rows start with the BOS token.""" + bos = 50258 + features = [ + {"input_features": np.zeros((80, 3000)), "labels": [bos, 1, 2]}, + {"input_features": np.zeros((80, 3000)), "labels": [99, 3, 4]}, # no BOS + ] + batch = collator(features) + assert batch["labels"][0, 0].item() == bos diff --git a/tests/test_finetune_metrics.py b/tests/test_finetune_metrics.py new file mode 100644 index 0000000..d53feeb --- /dev/null +++ b/tests/test_finetune_metrics.py @@ -0,0 +1,151 @@ +""" +test_finetune_metrics.py — Unit tests for listenr.finetune.metrics + +Tests the make_compute_metrics factory using a mocked evaluate.load so the +`evaluate` optional dependency does not need to be installed. +""" + +from __future__ import annotations + +import sys +from types import SimpleNamespace +from unittest.mock import MagicMock, patch + +import numpy as np +import pytest + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _make_tokenizer(decode_map: dict | None = None) -> MagicMock: + """Return a mock tokenizer whose batch_decode uses *decode_map*. + + *decode_map* maps a tuple of the token ids to the decoded string. + If not provided, the tokenizer returns ``" ".join(str(i) for i in ids)``. + """ + tokenizer = MagicMock() + tokenizer.pad_token_id = 50256 + + def _batch_decode(ids_list, skip_special_tokens=True): + if decode_map: + return [decode_map.get(tuple(row.tolist() if hasattr(row, "tolist") else row), "") + for row in ids_list] + return [" ".join(str(i) for i in (row.tolist() if hasattr(row, "tolist") else row)) + for row in ids_list] + + tokenizer.batch_decode.side_effect = _batch_decode + return tokenizer + + +def _make_pred(predictions, label_ids): + """Return an EvalPrediction-like namespace.""" + return SimpleNamespace( + predictions=np.array(predictions), + label_ids=np.array(label_ids, dtype=np.int64), + ) + + +# --------------------------------------------------------------------------- +# make_compute_metrics +# --------------------------------------------------------------------------- + +class TestMakeComputeMetrics: + @pytest.fixture() + def mock_evaluate(self): + """Inject a deterministic WER metric via sys.modules so the lazy import works.""" + metric = MagicMock() + + def _compute(predictions, references): + wrong = sum(p != r for p, r in zip(predictions, references)) + return wrong / len(references) if references else 0.0 + + metric.compute.side_effect = _compute + + mock_module = MagicMock() + mock_module.load.return_value = metric + + with patch.dict(sys.modules, {"evaluate": mock_module}): + yield mock_module, metric + + def test_returns_callable(self, mock_evaluate): + from listenr.finetune.metrics import make_compute_metrics + fn = make_compute_metrics(_make_tokenizer()) + assert callable(fn) + + def test_evaluate_load_called_with_wer(self, mock_evaluate): + mock_mod, _ = mock_evaluate + from listenr.finetune.metrics import make_compute_metrics + make_compute_metrics(_make_tokenizer()) + mock_mod.load.assert_called_once_with("wer") + + def test_perfect_predictions_give_zero_wer(self, mock_evaluate): + from listenr.finetune.metrics import make_compute_metrics + + tokenizer = MagicMock() + tokenizer.pad_token_id = 0 + # Both pred and label decode to the same string + tokenizer.batch_decode.return_value = ["hello world", "test"] + + fn = make_compute_metrics(tokenizer) + pred = _make_pred([[1, 2], [3, 4]], [[1, 2], [3, 4]]) + result = fn(pred) + + assert "wer" in result + assert result["wer"] == 0.0 + + def test_all_wrong_predictions_give_100_wer(self, mock_evaluate): + _, metric = mock_evaluate + # Override metric to return 1.0 (100%) for all-wrong case + metric.compute.return_value = 1.0 + + from listenr.finetune.metrics import make_compute_metrics + + tokenizer = MagicMock() + tokenizer.pad_token_id = 0 + tokenizer.batch_decode.side_effect = [ + ["hello", "world"], # predictions + ["wrong", "answer"], # labels + ] + + fn = make_compute_metrics(tokenizer) + pred = _make_pred([[1], [2]], [[3], [4]]) + result = fn(pred) + + assert result["wer"] == 100.0 + + def test_padding_tokens_replaced_before_decode(self, mock_evaluate): + """label_ids -100 positions must be replaced with pad_token_id before decode.""" + from listenr.finetune.metrics import make_compute_metrics + + pad_id = 50256 + tokenizer = MagicMock() + tokenizer.pad_token_id = pad_id + tokenizer.batch_decode.return_value = ["hello"] + + fn = make_compute_metrics(tokenizer) + # -100 at position 1 should become pad_id + label_ids = np.array([[1, -100, 3]]) + pred = _make_pred([[1, 2, 3]], label_ids) + fn(pred) + + # After replacement, no -100 should remain in what was passed to decode + decoded_label_arg = tokenizer.batch_decode.call_args_list[1][0][0] + assert -100 not in decoded_label_arg + + def test_result_wer_is_scaled_to_percent(self, mock_evaluate): + """WER should be stored as a percentage (metric returns 0–1, we multiply by 100).""" + _, metric = mock_evaluate + metric.compute.return_value = 0.35 # 35% + + from listenr.finetune.metrics import make_compute_metrics + + tokenizer = MagicMock() + tokenizer.pad_token_id = 0 + tokenizer.batch_decode.return_value = ["x"] + + fn = make_compute_metrics(tokenizer) + result = fn(_make_pred([[1]], [[1]])) + + assert abs(result["wer"] - 35.0) < 1e-6 diff --git a/tests/test_finetune_model.py b/tests/test_finetune_model.py new file mode 100644 index 0000000..e341d4b --- /dev/null +++ b/tests/test_finetune_model.py @@ -0,0 +1,173 @@ +""" +test_finetune_model.py — Unit tests for listenr.finetune.model + +Tests the LoRA config factory, encoder freezing, and parameter counting using +small synthetic torch modules — no GPU or model download required. +""" + +from __future__ import annotations + +from unittest.mock import MagicMock, patch + +import pytest +import torch +import torch.nn as nn + + +# --------------------------------------------------------------------------- +# make_lora_config +# --------------------------------------------------------------------------- + +class TestMakeLoraConfig: + def test_returns_lora_config_with_correct_values(self): + peft = pytest.importorskip("peft") + from listenr.finetune.model import make_lora_config + + cfg = make_lora_config(r=4, alpha=16, dropout=0.05, target_modules=["q_proj"]) + + assert cfg.r == 4 + assert cfg.lora_alpha == 16 + assert cfg.lora_dropout == 0.05 + assert cfg.target_modules == ["q_proj"] + + def test_task_type_is_seq2seq(self): + peft = pytest.importorskip("peft") + from peft import TaskType + from listenr.finetune.model import make_lora_config + + cfg = make_lora_config(r=8, alpha=32, dropout=0.1, target_modules=["v_proj"]) + assert cfg.task_type == TaskType.SEQ_2_SEQ_LM + + def test_inference_mode_is_false(self): + pytest.importorskip("peft") + from listenr.finetune.model import make_lora_config + + cfg = make_lora_config(r=8, alpha=32, dropout=0.1, target_modules=["q_proj"]) + assert cfg.inference_mode is False + + def test_multiple_target_modules(self): + pytest.importorskip("peft") + from listenr.finetune.model import make_lora_config + + cfg = make_lora_config(r=8, alpha=32, dropout=0.1, + target_modules=["q_proj", "v_proj"]) + assert set(cfg.target_modules) == {"q_proj", "v_proj"} + + +# --------------------------------------------------------------------------- +# freeze_encoder — tested with a small synthetic model +# --------------------------------------------------------------------------- + +class _FakeEncoder(nn.Module): + def __init__(self): + super().__init__() + self.weight = nn.Parameter(torch.randn(4, 4)) + + +class _FakeDecoder(nn.Module): + def __init__(self): + super().__init__() + self.weight = nn.Parameter(torch.randn(4, 4)) + + +class _FakeWhisperInner(nn.Module): + def __init__(self): + super().__init__() + self.encoder = _FakeEncoder() + self.decoder = _FakeDecoder() # unused but present for realism + + +class _FakeWhisper(nn.Module): + """Mimics the attribute path used by freeze_encoder on a raw Whisper model.""" + def __init__(self): + super().__init__() + self.model = _FakeWhisperInner() + + +class _FakePeftWhisper(nn.Module): + """Mimics the attribute path used by freeze_encoder on a PeftModel.""" + def __init__(self): + super().__init__() + self.base_model = MagicMock() + inner = _FakeWhisperInner() + self.base_model.model.model = inner + # Give access to encoder params for assertion + self._inner = inner + + +class TestFreezeEncoder: + def test_encoder_params_frozen_on_raw_model(self): + from listenr.finetune.model import freeze_encoder + + model = _FakeWhisper() + # Confirm trainable before freezing + assert model.model.encoder.weight.requires_grad + + freeze_encoder(model) + + assert not model.model.encoder.weight.requires_grad + + def test_decoder_params_still_trainable_after_freeze(self): + from listenr.finetune.model import freeze_encoder + + model = _FakeWhisper() + freeze_encoder(model) + + # Decoder should be unaffected + assert model.model.decoder.weight.requires_grad + + def test_encoder_params_frozen_on_peft_model(self): + from listenr.finetune.model import freeze_encoder + + model = _FakePeftWhisper() + # Confirm trainable before + assert model._inner.encoder.weight.requires_grad + + freeze_encoder(model) + + assert not model._inner.encoder.weight.requires_grad + + +# --------------------------------------------------------------------------- +# count_trainable_params +# --------------------------------------------------------------------------- + +class TestCountTrainableParams: + def test_all_params_trainable_by_default(self): + from listenr.finetune.model import count_trainable_params + + model = nn.Linear(4, 4) # weight (16) + bias (4) = 20 params + trainable, total = count_trainable_params(model) + assert trainable == total == 20 + + def test_frozen_params_not_counted_as_trainable(self): + from listenr.finetune.model import count_trainable_params + + model = nn.Linear(4, 4) + for p in model.parameters(): + p.requires_grad = False + + trainable, total = count_trainable_params(model) + assert trainable == 0 + assert total == 20 + + def test_partial_freeze(self): + from listenr.finetune.model import count_trainable_params + + model = nn.Sequential(nn.Linear(4, 4), nn.Linear(4, 4)) + # Freeze only the first sub-module + for p in model[0].parameters(): + p.requires_grad = False + + trainable, total = count_trainable_params(model) + assert total == 40 # two Linear(4,4) + assert trainable == 20 # only second layer + + def test_returns_tuple_of_two_ints(self): + from listenr.finetune.model import count_trainable_params + + model = nn.Linear(2, 2) + result = count_trainable_params(model) + assert isinstance(result, tuple) + assert len(result) == 2 + assert all(isinstance(v, int) for v in result) From 5475798a7ed32809f19670dc971aba8a96e61b63 Mon Sep 17 00:00:00 2001 From: rebreda Date: Wed, 4 Mar 2026 21:29:24 -0500 Subject: [PATCH 02/11] update tests and comments --- src/listenr/finetune/data.py | 8 +- src/listenr/finetune/metrics.py | 4 +- src/listenr/finetune/model.py | 8 +- src/listenr/finetune/train.py | 2 +- tests/test_finetune_metrics.py | 2 + tests/test_finetune_model.py | 3 +- uv.lock | 1452 ++++++++++++++++++++++++++++++- 7 files changed, 1466 insertions(+), 13 deletions(-) diff --git a/src/listenr/finetune/data.py b/src/listenr/finetune/data.py index 6fc028e..bb3375e 100644 --- a/src/listenr/finetune/data.py +++ b/src/listenr/finetune/data.py @@ -6,7 +6,7 @@ Requires the ``finetune`` optional dependencies:: - uv pip install "listenr[finetune]" + uv pip install -e ".[finetune]" Public API ---------- @@ -49,7 +49,7 @@ def make_processor(model_id: str, language: str, task: str) -> "WhisperProcessor except ImportError: print( "ERROR: transformers is required. Install with:\n" - " uv pip install \"listenr[finetune]\"", + " uv pip install -e \".[finetune]\"", file=sys.stderr, ) sys.exit(1) @@ -105,7 +105,7 @@ def make_dataset(hf_dataset_path: Path, processor: Any) -> Any: except ImportError: print( "ERROR: datasets is required. Install with:\n" - " uv pip install \"listenr[finetune]\"", + " uv pip install -e \".[finetune]\"", file=sys.stderr, ) sys.exit(1) @@ -151,7 +151,7 @@ def __call__(self, features: list[dict]) -> dict: except ImportError: print( "ERROR: torch is required. Install with:\n" - " uv pip install \"listenr[finetune]\"", + " uv pip install -e \".[finetune]\"", file=sys.stderr, ) sys.exit(1) diff --git a/src/listenr/finetune/metrics.py b/src/listenr/finetune/metrics.py index d602123..07e74fa 100644 --- a/src/listenr/finetune/metrics.py +++ b/src/listenr/finetune/metrics.py @@ -6,7 +6,7 @@ Requires the ``finetune`` optional dependencies:: - uv pip install "listenr[finetune]" + uv pip install -e ".[finetune]" Public API ---------- @@ -43,7 +43,7 @@ def make_compute_metrics(tokenizer: Any) -> Callable: except ImportError: print( "ERROR: evaluate is required. Install with:\n" - " uv pip install \"listenr[finetune]\"", + " uv pip install -e \".[finetune]\"", file=sys.stderr, ) sys.exit(1) diff --git a/src/listenr/finetune/model.py b/src/listenr/finetune/model.py index 78f4ddf..992bd01 100644 --- a/src/listenr/finetune/model.py +++ b/src/listenr/finetune/model.py @@ -6,7 +6,7 @@ Requires the ``finetune`` optional dependencies:: - uv pip install "listenr[finetune]" + uv pip install -e ".[finetune]" Public API ---------- @@ -38,7 +38,7 @@ def load_base_model(model_id: str) -> Any: except ImportError: print( "ERROR: transformers is required. Install with:\n" - " uv pip install \"listenr[finetune]\"", + " uv pip install -e \".[finetune]\"", file=sys.stderr, ) sys.exit(1) @@ -78,7 +78,7 @@ def make_lora_config( except ImportError: print( "ERROR: peft is required. Install with:\n" - " uv pip install \"listenr[finetune]\"", + " uv pip install -e \".[finetune]\"", file=sys.stderr, ) sys.exit(1) @@ -109,7 +109,7 @@ def apply_lora(model: Any, lora_config: Any) -> Any: except ImportError: print( "ERROR: peft is required. Install with:\n" - " uv pip install \"listenr[finetune]\"", + " uv pip install -e \".[finetune]\"", file=sys.stderr, ) sys.exit(1) diff --git a/src/listenr/finetune/train.py b/src/listenr/finetune/train.py index c798b16..5d7e97a 100644 --- a/src/listenr/finetune/train.py +++ b/src/listenr/finetune/train.py @@ -173,7 +173,7 @@ def main() -> None: except ImportError: print( "ERROR: transformers is required. Install with:\n" - " uv pip install \"listenr[finetune]\"", + " uv pip install -e \".[finetune]\"", file=sys.stderr, ) sys.exit(1) diff --git a/tests/test_finetune_metrics.py b/tests/test_finetune_metrics.py index d53feeb..ed64963 100644 --- a/tests/test_finetune_metrics.py +++ b/tests/test_finetune_metrics.py @@ -137,6 +137,8 @@ def test_padding_tokens_replaced_before_decode(self, mock_evaluate): def test_result_wer_is_scaled_to_percent(self, mock_evaluate): """WER should be stored as a percentage (metric returns 0–1, we multiply by 100).""" _, metric = mock_evaluate + # Clear the fixture's side_effect so return_value is actually used. + metric.compute.side_effect = None metric.compute.return_value = 0.35 # 35% from listenr.finetune.metrics import make_compute_metrics diff --git a/tests/test_finetune_model.py b/tests/test_finetune_model.py index e341d4b..c38e5c0 100644 --- a/tests/test_finetune_model.py +++ b/tests/test_finetune_model.py @@ -28,7 +28,8 @@ def 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+++++++- 3 files changed, 57 insertions(+), 6 deletions(-) diff --git a/src/listenr/build_dataset.py b/src/listenr/build_dataset.py index 1f2938e..b8f055b 100644 --- a/src/listenr/build_dataset.py +++ b/src/listenr/build_dataset.py @@ -202,7 +202,7 @@ def write_csv(entries: list[dict], output_dir: Path, split: str) -> Path: def write_hf_dataset(entries: list[dict], output_dir: Path) -> None: """Write a HuggingFace DatasetDict to output_dir (requires 'datasets' package).""" try: - from datasets import Dataset, DatasetDict, Audio as HFAudio # type: ignore + from datasets import Dataset, DatasetDict # type: ignore except ImportError: logger.error( "The 'datasets' package is required for HuggingFace format. " @@ -218,8 +218,10 @@ def write_hf_dataset(entries: list[dict], output_dir: Path) -> None: for split_name, split_entries in splits_dict.items(): if not split_entries: continue + # Keep audio_path as a plain string so datasets never tries to decode + # it automatically (datasets 4+ requires torchcodec for Audio features). + # prepare_example loads the WAV on-the-fly with soundfile instead. ds = Dataset.from_list(split_entries) - ds = ds.cast_column("audio_path", HFAudio(sampling_rate=16000)) hf_splits[split_name] = ds dd = DatasetDict(hf_splits) @@ -308,6 +310,17 @@ def main() -> None: default=not DEFAULT_STRIP_TAGS, help="Preserve parenthesised/bracketed noise tags (e.g. (music)) in transcriptions", ) + parser.add_argument( + "--remap-audio-prefix", + metavar="OLD:NEW", + default=None, + help=( + "Rewrite the leading path component of every audio_path in the manifest. " + "Useful when running inside a container where the host path is mounted at " + "a different location. Example: " + "--remap-audio-prefix /home/g/.listenr/audio_clips:/data/listenr/audio_clips" + ), + ) parser.add_argument( "--dry-run", action="store_true", @@ -315,6 +328,16 @@ def main() -> None: ) args = parser.parse_args() + # Parse the prefix remap if provided + remap_old: str | None = None + remap_new: str | None = None + if args.remap_audio_prefix: + parts = args.remap_audio_prefix.split(":", 1) + if len(parts) != 2 or not parts[0] or not parts[1]: + logger.error("--remap-audio-prefix must be in the form OLD:NEW (two non-empty paths separated by ':')") + sys.exit(1) + remap_old, remap_new = parts[0], parts[1] + manifest_path = args.manifest or _manifest_path() output_dir = Path(args.output).expanduser() @@ -327,6 +350,15 @@ def main() -> None: records = load_manifest(manifest_path) logger.info(f"Loaded {len(records)} records from {manifest_path}") + if remap_old is not None: + remapped = 0 + for rec in records: + p = rec.get("audio_path", "") + if p.startswith(remap_old): + rec["audio_path"] = remap_new + p[len(remap_old):] + remapped += 1 + logger.info(f"Remapped {remapped}/{len(records)} audio paths: {remap_old!r} -> {remap_new!r}") + entries = [] skipped = 0 for rec in records: diff --git a/src/listenr/finetune/data.py b/src/listenr/finetune/data.py index bb3375e..435e514 100644 --- a/src/listenr/finetune/data.py +++ b/src/listenr/finetune/data.py @@ -65,8 +65,11 @@ def prepare_example(batch: dict, processor: Any) -> dict: """Convert a single dataset example into model-ready tensors. Expects the dataset to have been created by ``listenr-build-dataset --format hf`` - (or ``both``), so the ``audio_path`` column is a decoded HuggingFace - :class:`datasets.Audio` dict with keys ``array`` and ``sampling_rate``. + (or ``both``). ``audio_path`` may be either: + + * A plain file-path string — loaded on-the-fly with ``soundfile``. + * A decoded HuggingFace :class:`datasets.Audio` dict with keys + ``array`` and ``sampling_rate`` — used directly (legacy / test usage). Returns a dict with: @@ -76,7 +79,17 @@ def prepare_example(batch: dict, processor: Any) -> dict: ``labels`` Token ids for ``corrected_transcription``. """ - audio = batch["audio_path"] # decoded Audio dict: {"array": ndarray, "sampling_rate": int} + audio = batch["audio_path"] + + # Handle plain path string — load with soundfile to avoid the torchcodec + # requirement introduced in datasets 4+. + if isinstance(audio, str): + import soundfile as sf # already a listenr core dependency + array, sample_rate = sf.read(audio, dtype="float32") + if array.ndim > 1: # stereo → mono + array = array.mean(axis=1) + audio = {"array": array, "sampling_rate": sample_rate} + input_features = processor.feature_extractor( audio["array"], sampling_rate=audio["sampling_rate"], diff --git a/src/listenr/finetune/train.py b/src/listenr/finetune/train.py index 5d7e97a..e6e4317 100644 --- a/src/listenr/finetune/train.py +++ b/src/listenr/finetune/train.py @@ -156,6 +156,12 @@ def main() -> None: default=FINETUNE_BF16, help="Enable bf16 mixed precision (recommended for AMD ROCm RDNA2+)", ) + parser.add_argument( + "--report-to", + default="tensorboard", + metavar="BACKEND", + help="Reporting backend(s) for the Trainer, e.g. 'tensorboard', 'wandb', or 'none' (default: tensorboard)", + ) # --- misc --- parser.add_argument( @@ -266,7 +272,7 @@ def main() -> None: save_steps=args.save_steps, eval_steps=args.eval_steps, logging_steps=max(1, args.eval_steps // 4), - report_to=["tensorboard"], + report_to=args.report_to, load_best_model_at_end=True, metric_for_best_model="wer", greater_is_better=False, From 8c9158bd85836045e15be877ca013d8f528b7279 Mon Sep 17 00:00:00 2001 From: rebreda Date: Sat, 7 Mar 2026 07:22:38 -0500 Subject: [PATCH 04/11] Add docker --- .dockerignore | 15 ++ .env.example | 33 ++++ Dockerfile | 392 +++++++++++++++++++++++++++++++++++++++++++++ docker-compose.yml | 72 +++++++++ 4 files changed, 512 insertions(+) create mode 100644 .dockerignore create mode 100644 .env.example create mode 100644 Dockerfile create mode 100644 docker-compose.yml diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..01694e4 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,15 @@ +.git +.venv +.env +__pycache__ +*.pyc +*.pyo +*.egg-info +dist +build +.pytest_cache +.mypy_cache +.ruff_cache +tests/ +*.md +screenshot.png diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..7a44ca7 --- /dev/null +++ b/.env.example @@ -0,0 +1,33 @@ +# Copy this file to .env and edit to suit your system. +# .env is gitignored — .env.example is the reference. + +# ── GPU ─────────────────────────────────────────────────────────────────────── + +# Which GPU(s) ROCm should use. "0" = first GPU only (recommended for +# multi-GPU systems with unmatched cards, avoids imbalance segfaults). +# Set to "0,1" to use both GPUs if they are the same model. +HIP_VISIBLE_DEVICES=0 + +# Uncomment and set if your GPU's gfx version isn't natively supported. +# Check with: rocm-smi --showproductname +# RX 6000 series (RDNA2): 10.3.0 +# RX 7000 series (RDNA3): 11.0.0 +# HSA_OVERRIDE_GFX_VERSION=10.3.0 + +# ── paths (override if your data lives elsewhere) ───────────────────────────── + +# Host directory containing audio_clips/ and manifest.jsonl +# Default: ~/.listenr +# LISTENR_DATA=/home/you/.listenr + +# Host directory for train/dev/test dataset splits (written by build-dataset) +# Default: ~/listenr_dataset +# LISTENR_DATASET=/home/you/listenr_dataset + +# Host directory for LoRA adapter checkpoints (written by finetune) +# Default: ~/listenr_finetune +# LISTENR_FINETUNE=/home/you/listenr_finetune + +# HuggingFace model cache (shared with host to avoid re-downloads) +# Default: ~/.cache/huggingface +# HF_CACHE=/home/you/.cache/huggingface diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..d090001 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,392 @@ +# Running Lemonade C++ in Docker + +## Quick Start with Docker + +> You may need additional configuration depending on your environment. + +### Docker Run with Default Configuration + +```bash +docker run -d \ + --name lemonade-server \ + -p 8000:8000 \ + -v lemonade-cache:/root/.cache/huggingface \ + -v lemonade-llama:/opt/lemonade/llama \ + -e LEMONADE_LLAMACPP_BACKEND=cpu \ + ghcr.io/lemonade-sdk/lemonade-server:latest +``` + +### Docker Run with a Specific Port and Version + +```bash +docker run -d \ + --name lemonade-server \ + -p 4000:5000 \ + -v lemonade-cache:/root/.cache/huggingface \ + -v lemonade-llama:/opt/lemonade/llama \ + -e LEMONADE_LLAMACPP_BACKEND=cpu \ + ghcr.io/lemonade-sdk/lemonade-server:v9.1.3 \ + ./lemonade-server serve --no-tray --host 0.0.0.0 --port 5000 +``` + +> This will run the server on port 5000 inside the container, mapped to port 4000 on your host. + +### Other Docker Methods + +#### Docker Compose Setup +Docker Compose makes it easier to manage multi-container applications. +1. Make sure you have Docker Compose installed. +2. Create a `docker-compose.yml` file like this: + +```yml +services: + lemonade: + image: ghcr.io/lemonade-sdk/lemonade-server:latest + container_name: lemonade-server + ports: + - "8000:8000" + volumes: + # Persist downloaded models + - lemonade-cache:/root/.cache/huggingface + # # Persist llama binaries + - lemonade-llama:/opt/lemonade/llama + environment: + - LEMONADE_LLAMACPP_BACKEND=cpu + restart: unless-stopped + +volumes: + lemonade-cache: + lemonade-llama: +``` + +> You can add more services as needed. + +3. Run the following command in the directory containing your docker-compose.yml: + +```bash +docker-compose up -d +``` + +This will pull the latest image (or the version you specified) from the Lemonade container registry and start the server with your mapped ports. + +Once the container is running, verify it’s working: + +```bash +curl http://localhost:8000/api/v1/models +``` + +You should receive a response listing available models. + +
+ +# Build Your Own Docker Image +Documentation below shows container based workflows and how to build your own environments if needed. + +## Container-based workflows + +This repository supports two container-related workflows with different goals: + +### Development (Dev Containers) +The `.devcontainer` ([dev container](https://github.com/lemonade-sdk/lemonade/blob/main/docs/dev-getting-started.md#developer-ide--ide-build-steps)) configuration is intended for contributors and developers. +It provides a full development environment (tooling, debuggers, source mounted) +and is primarily used with VS Code Dev Containers or GitHub Codespaces. + +### Running Lemonade in a container +The Dockerfile and `docker-compose.yml` guide provided here are intended for running +Lemonade as an application in a containerized environment. This uses a +multi-stage build to produce a minimal runtime image, similar in spirit to the +MSI-based distribution, but containerized. + +These workflows are complementary and serve different use cases. + +## Lemonade C++ Docker Setup +This guide explains how to build and run Lemonade C++ in a Docker container using Docker Compose. The setup includes persistent caching for HuggingFace models. + +> If you want to pull or use a specific Lemonade Docker image instead of building your own, check out the instructions in `README.md` + +--- + +### Prerequisites +- Docker >= 24.x +- Docker Compose >= 2.x +- At least 8 GB RAM and 4 CPU cores recommended for small models +- Internet access to download model files from HuggingFace + +--- + +### 1. Docker File +The Dockerfile below uses a **multi-stage build** to compile Lemonade C++ components and produce a clean, lightweight runtime image. + +Place the Dockerfile in the parent directory of the repository root when building. + +> **Build context note** +> +> This guide assumes the Dockerfile and `docker-compose.yml` live outside the Lemonade repository directory. +> Like below +>```css +>. +>├── docker-compose.yml +>├── Dockerfile +>└── lemonade/ +> ├── src +> ├── docs +> ├── .devcontainer +> └── ... +>``` +> If you place them inside the repository, +> update the Dockerfile to use `COPY . /app` instead. + +This configuration has been tested with both the Vulkan and CPU backends and you can modify or extend it to suit your specific deployment needs. + +```dockerfile +# ============================================================== +# # 1. Build stage — compile lemonade C++ binaries +# # ============================================================ +FROM ubuntu:24.04 AS builder + +# Avoid interactive prompts during build +ENV DEBIAN_FRONTEND=noninteractive + +# Install build dependencies +RUN apt-get update && apt-get install -y \ + build-essential \ + cmake \ + libssl-dev \ + pkg-config \ + git \ + && rm -rf /var/lib/apt/lists/* + +# Copy source code +COPY lemonade /app +WORKDIR /app/ + +# Build the project +RUN rm -rf build && \ + mkdir -p build && \ + cd build && \ + cmake .. && \ + cmake --build . --config Release -j"$(nproc)" + +# Debug: Check build outputs +RUN echo "=== Build directory contents ===" && \ + ls -la build/ && \ + echo "=== Checking for resources ===" && \ + find build/ -name "*.json" -o -name "resources" -type d + +# # ============================================================ +# # 2. Runtime stage — small, clean image +# # ============================================================ +FROM ubuntu:24.04 + +# Install runtime dependencies only +RUN apt-get update && apt-get install -y \ + libcurl4 \ + curl \ + libssl3 \ + zlib1g \ + vulkan-tools \ + libvulkan1 \ + unzip \ + libgomp1 \ + && rm -rf /var/lib/apt/lists/* + +# Create application directory +WORKDIR /opt/lemonade + +# Copy built executables and resources from builder +COPY --from=builder /app/build/lemonade-router ./lemonade-router +COPY --from=builder /app/build/lemonade-server ./lemonade-server +COPY --from=builder /app/build/resources ./resources + +# Make executables executable +RUN chmod +x ./lemonade-router ./lemonade-server + +# Create necessary directories +RUN mkdir -p /opt/lemonade/llama/cpu \ + /opt/lemonade/llama/vulkan \ + /root/.cache/huggingface + +# Expose default port +EXPOSE 8000 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ + CMD curl -f http://localhost:8000/live || exit 1 + +# Default command: start server in headless mode +CMD ["./lemonade-server", "serve", "--no-tray", "--host", "0.0.0.0"] +``` + +### 2. Build the Docker Image + +Create below `docker-compose.yml` file within the parent directory of repository root (where Dockerfile is located): + +```yml +services: + lemonade: + build: + context: . + dockerfile: Dockerfile + container_name: lemonade-server + ports: + - "8000:8000" + volumes: + # Persist downloaded models + - lemonade-cache:/root/.cache/huggingface + # # Persist llama binaries + - lemonade-llama:/opt/lemonade/llama + environment: + - LEMONADE_LLAMACPP_BACKEND=cpu + restart: unless-stopped + +volumes: + lemonade-cache: + lemonade-llama: + +``` + +Now run below command within the same directory: + +```bash +docker-compose build +``` + +This will: + +- Compile Lemonade C++ (lemonade-server and lemonade-router) +- Prepare a runtime image with all dependencies + +### 3. Run the Container + +Start the container with Docker Compose: + +```bash +docker-compose up -d +``` + +- The API will be exposed on port 8000 +- HuggingFace models will be cached in the lemonade-cache volume +- LLaMA binaries are persisted in lemonade-llama volume + +Check that the server is running: + +```bash +docker logs -f lemonade-server +``` + +You should see: + +```bash +lemonade-server | Lemonade Server vx.x.x started on port 8000 +lemonade-server | Chat and manage models: http://localhost:8000 +``` + +--- + +### 4. Access the API + +Test the API: +```bash +curl http://localhost:8000/api/v1/models +``` + +You should get a response with available models. + +### 5. Load a Model + +You can use the gui on localhost:8000 or below command to load a model (e.g., Qwen 0.6B): + +```bash +curl -X POST http://localhost:8000/api/v1/load \ + -H "Content-Type: application/json" \ + -d '{"model_name": "Qwen3-0.6B-GGUF"}' +``` + +The server will: +- Auto-download the GGUF model from HuggingFace +- Install the backend +- Make the model ready for inference + +### 6. Make a Chat Request + +Once the model is loaded: + +```python +from openai import OpenAI + +client = OpenAI( + base_url="http://localhost:8000/api/v1", + api_key="lemonade" # required but unused +) + +completion = client.chat.completions.create( + model="Qwen3-0.6B-GGUF", + messages=[{"role": "user", "content": "Hello, Lemonade!"}] +) + +print(completion.choices[0].message.content) +``` + +### 7. Stopping the Server + +```bash +docker-compose down +``` + +- Keeps cached models and binaries in Docker volumes +- You can restart anytime with docker-compose up -d + +### 8. Troubleshooting + +Server not starting: Check logs with: + +```bash +docker logs lemonade-server +``` + +- Model download fails: Ensure /root/.cache/huggingface volume is writable +- Vulkan errors on CPU-only machine: The server will fallback to CPU backend automatically# Listenr — AMD ROCm fine-tuning image +# +# Base: official AMD ROCm PyTorch image (Python 3.12, ROCm-aware PyTorch pre-built). +# Pull: podman pull rocm/pytorch:latest +# Build: podman build -t listenr-rocm . +# Run: podman compose run --rm finetune +# +# NOTE: The rocm/pytorch base ships Python 3.12. listenr's pyproject.toml +# requires Python >=3.13. We install with --ignore-requires-python +# because the codebase uses no 3.13-only syntax — the constraint exists +# to encourage modern local installs, not to block Docker usage. +# +# NOTE: sounddevice (microphone capture) will not work inside this container. +# This image is intended for fine-tuning only (listenr-finetune / +# listenr-build-dataset), not real-time audio capture. + +FROM rocm/pytorch:latest + +# ── system packages ────────────────────────────────────────────────────────── +# libsndfile1 : required by soundfile (audio I/O in finetune data pipeline) +# ffmpeg : optional but useful for converting audio files +RUN apt-get update && apt-get install -y --no-install-recommends \ + libsndfile1 \ + ffmpeg \ + && rm -rf /var/lib/apt/lists/* + +# ── project install ────────────────────────────────────────────────────────── +WORKDIR /app +COPY . /app + +# Install core + finetune extras. +# --ignore-requires-python: base image has Python 3.10; constraint is 3.13+ +# but no 3.13-specific syntax is used in this codebase. +RUN pip install --no-cache-dir \ + --ignore-requires-python \ + -e ".[finetune]" + +# ── runtime defaults ───────────────────────────────────────────────────────── +# Override HSA_OVERRIDE_GFX_VERSION if your GPU reports an unsupported gfx. +# Set HIP_VISIBLE_DEVICES=0 to restrict to a single GPU (avoids imbalance +# warnings and segfaults on multi-GPU systems). +ENV HSA_OVERRIDE_GFX_VERSION="" \ + HIP_VISIBLE_DEVICES="0" + +CMD ["listenr-finetune", "--help"] diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..3bac47a --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,72 @@ +# docker-compose.yml — Listenr AMD ROCm fine-tuning +# +# Usage (requires podman-compose or docker compose): +# podman compose build +# podman compose run --rm finetune # fine-tune with defaults +# podman compose run --rm finetune --bf16 --max-steps 500 +# podman compose run --rm build-dataset # build HF dataset from manifest +# +# GPU passthrough requires the ROCm driver on the host. +# Copy .env.example to .env and edit values to suit your system. + +services: + + # ── fine-tune Whisper with LoRA ──────────────────────────────────────────── + finetune: + build: . + image: listenr-rocm + # Required for ROCm GPU access and stability + cap_add: + - SYS_PTRACE + security_opt: + - seccomp=unconfined + ipc: host + devices: + - /dev/kfd # AMD GPU compute + - /dev/dri # AMD GPU render + group_add: + - video + - render + environment: + # Pin to one GPU to avoid imbalance warnings/segfaults on multi-GPU systems. + # Set to "0,1" to use both GPUs if they are matched. + HIP_VISIBLE_DEVICES: "${HIP_VISIBLE_DEVICES:-0}" + # Override if your GPU's gfx version isn't natively supported by ROCm. + # Leave blank to let ROCm auto-detect. + HSA_OVERRIDE_GFX_VERSION: "${HSA_OVERRIDE_GFX_VERSION:-}" + volumes: + # HF dataset produced by build-dataset (DATASET_OUTPUT default: ~/listenr_dataset) + - "${LISTENR_DATASET:-${HOME}/listenr_dataset}:/data/dataset" + # Adapter checkpoint output (FINETUNE_OUTPUT_DIR default: ~/listenr_finetune) + - "${LISTENR_FINETUNE:-${HOME}/listenr_finetune}:/data/adapter" + # HuggingFace model cache — avoids re-downloading on every run + - "${HF_CACHE:-${HOME}/.cache/huggingface}:/root/.cache/huggingface" + working_dir: /app + entrypoint: ["listenr-finetune"] + command: + - "--dataset=/data/dataset/hf_dataset" + - "--output=/data/adapter" + - "--bf16" + + # ── build train/dev/test splits from manifest.jsonl ─────────────────────── + build-dataset: + build: . + image: listenr-rocm + cap_add: + - SYS_PTRACE + security_opt: + - seccomp=unconfined + volumes: + # Audio clips + manifest.jsonl (STORAGE_BASE default: ~/.listenr/audio_clips) + - "${LISTENR_DATA:-${HOME}/.listenr}:/data/listenr" + # Dataset output (DATASET_OUTPUT default: ~/listenr_dataset) + - "${LISTENR_DATASET:-${HOME}/listenr_dataset}:/data/dataset" + working_dir: /app + entrypoint: ["listenr-build-dataset"] + command: + - "--manifest=/data/listenr/audio_clips/manifest.jsonl" + - "--output=/data/dataset" + - "--format=hf" + # Rewrites host absolute paths in manifest.jsonl to container paths. + # OLD is the host prefix stored in manifest entries; NEW is where it's mounted here. + - "--remap-audio-prefix=${HOME}/.listenr/audio_clips:/data/listenr/audio_clips" From 54c764fedb27f41b2bdb02f9faf565f14af46408 Mon Sep 17 00:00:00 2001 From: rebreda Date: Sat, 7 Mar 2026 07:23:01 -0500 Subject: [PATCH 05/11] Add docker --- Dockerfile | 349 +---------------------------------------------------- 1 file changed, 1 insertion(+), 348 deletions(-) diff --git a/Dockerfile b/Dockerfile index d090001..c05e632 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,351 +1,4 @@ -# Running Lemonade C++ in Docker - -## Quick Start with Docker - -> You may need additional configuration depending on your environment. - -### Docker Run with Default Configuration - -```bash -docker run -d \ - --name lemonade-server \ - -p 8000:8000 \ - -v lemonade-cache:/root/.cache/huggingface \ - -v lemonade-llama:/opt/lemonade/llama \ - -e LEMONADE_LLAMACPP_BACKEND=cpu \ - ghcr.io/lemonade-sdk/lemonade-server:latest -``` - -### Docker Run with a Specific Port and Version - -```bash -docker run -d \ - --name lemonade-server \ - -p 4000:5000 \ - -v lemonade-cache:/root/.cache/huggingface \ - -v lemonade-llama:/opt/lemonade/llama \ - -e LEMONADE_LLAMACPP_BACKEND=cpu \ - ghcr.io/lemonade-sdk/lemonade-server:v9.1.3 \ - ./lemonade-server serve --no-tray --host 0.0.0.0 --port 5000 -``` - -> This will run the server on port 5000 inside the container, mapped to port 4000 on your host. - -### Other Docker Methods - -#### Docker Compose Setup -Docker Compose makes it easier to manage multi-container applications. -1. Make sure you have Docker Compose installed. -2. Create a `docker-compose.yml` file like this: - -```yml -services: - lemonade: - image: ghcr.io/lemonade-sdk/lemonade-server:latest - container_name: lemonade-server - ports: - - "8000:8000" - volumes: - # Persist downloaded models - - lemonade-cache:/root/.cache/huggingface - # # Persist llama binaries - - lemonade-llama:/opt/lemonade/llama - environment: - - LEMONADE_LLAMACPP_BACKEND=cpu - restart: unless-stopped - -volumes: - lemonade-cache: - lemonade-llama: -``` - -> You can add more services as needed. - -3. Run the following command in the directory containing your docker-compose.yml: - -```bash -docker-compose up -d -``` - -This will pull the latest image (or the version you specified) from the Lemonade container registry and start the server with your mapped ports. - -Once the container is running, verify it’s working: - -```bash -curl http://localhost:8000/api/v1/models -``` - -You should receive a response listing available models. - -
- -# Build Your Own Docker Image -Documentation below shows container based workflows and how to build your own environments if needed. - -## Container-based workflows - -This repository supports two container-related workflows with different goals: - -### Development (Dev Containers) -The `.devcontainer` ([dev container](https://github.com/lemonade-sdk/lemonade/blob/main/docs/dev-getting-started.md#developer-ide--ide-build-steps)) configuration is intended for contributors and developers. -It provides a full development environment (tooling, debuggers, source mounted) -and is primarily used with VS Code Dev Containers or GitHub Codespaces. - -### Running Lemonade in a container -The Dockerfile and `docker-compose.yml` guide provided here are intended for running -Lemonade as an application in a containerized environment. This uses a -multi-stage build to produce a minimal runtime image, similar in spirit to the -MSI-based distribution, but containerized. - -These workflows are complementary and serve different use cases. - -## Lemonade C++ Docker Setup -This guide explains how to build and run Lemonade C++ in a Docker container using Docker Compose. The setup includes persistent caching for HuggingFace models. - -> If you want to pull or use a specific Lemonade Docker image instead of building your own, check out the instructions in `README.md` - ---- - -### Prerequisites -- Docker >= 24.x -- Docker Compose >= 2.x -- At least 8 GB RAM and 4 CPU cores recommended for small models -- Internet access to download model files from HuggingFace - ---- - -### 1. Docker File -The Dockerfile below uses a **multi-stage build** to compile Lemonade C++ components and produce a clean, lightweight runtime image. - -Place the Dockerfile in the parent directory of the repository root when building. - -> **Build context note** -> -> This guide assumes the Dockerfile and `docker-compose.yml` live outside the Lemonade repository directory. -> Like below ->```css ->. ->├── docker-compose.yml ->├── Dockerfile ->└── lemonade/ -> ├── src -> ├── docs -> ├── .devcontainer -> └── ... ->``` -> If you place them inside the repository, -> update the Dockerfile to use `COPY . /app` instead. - -This configuration has been tested with both the Vulkan and CPU backends and you can modify or extend it to suit your specific deployment needs. - -```dockerfile -# ============================================================== -# # 1. Build stage — compile lemonade C++ binaries -# # ============================================================ -FROM ubuntu:24.04 AS builder - -# Avoid interactive prompts during build -ENV DEBIAN_FRONTEND=noninteractive - -# Install build dependencies -RUN apt-get update && apt-get install -y \ - build-essential \ - cmake \ - libssl-dev \ - pkg-config \ - git \ - && rm -rf /var/lib/apt/lists/* - -# Copy source code -COPY lemonade /app -WORKDIR /app/ - -# Build the project -RUN rm -rf build && \ - mkdir -p build && \ - cd build && \ - cmake .. && \ - cmake --build . --config Release -j"$(nproc)" - -# Debug: Check build outputs -RUN echo "=== Build directory contents ===" && \ - ls -la build/ && \ - echo "=== Checking for resources ===" && \ - find build/ -name "*.json" -o -name "resources" -type d - -# # ============================================================ -# # 2. Runtime stage — small, clean image -# # ============================================================ -FROM ubuntu:24.04 - -# Install runtime dependencies only -RUN apt-get update && apt-get install -y \ - libcurl4 \ - curl \ - libssl3 \ - zlib1g \ - vulkan-tools \ - libvulkan1 \ - unzip \ - libgomp1 \ - && rm -rf /var/lib/apt/lists/* - -# Create application directory -WORKDIR /opt/lemonade - -# Copy built executables and resources from builder -COPY --from=builder /app/build/lemonade-router ./lemonade-router -COPY --from=builder /app/build/lemonade-server ./lemonade-server -COPY --from=builder /app/build/resources ./resources - -# Make executables executable -RUN chmod +x ./lemonade-router ./lemonade-server - -# Create necessary directories -RUN mkdir -p /opt/lemonade/llama/cpu \ - /opt/lemonade/llama/vulkan \ - /root/.cache/huggingface - -# Expose default port -EXPOSE 8000 - -# Health check -HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ - CMD curl -f http://localhost:8000/live || exit 1 - -# Default command: start server in headless mode -CMD ["./lemonade-server", "serve", "--no-tray", "--host", "0.0.0.0"] -``` - -### 2. Build the Docker Image - -Create below `docker-compose.yml` file within the parent directory of repository root (where Dockerfile is located): - -```yml -services: - lemonade: - build: - context: . - dockerfile: Dockerfile - container_name: lemonade-server - ports: - - "8000:8000" - volumes: - # Persist downloaded models - - lemonade-cache:/root/.cache/huggingface - # # Persist llama binaries - - lemonade-llama:/opt/lemonade/llama - environment: - - LEMONADE_LLAMACPP_BACKEND=cpu - restart: unless-stopped - -volumes: - lemonade-cache: - lemonade-llama: - -``` - -Now run below command within the same directory: - -```bash -docker-compose build -``` - -This will: - -- Compile Lemonade C++ (lemonade-server and lemonade-router) -- Prepare a runtime image with all dependencies - -### 3. Run the Container - -Start the container with Docker Compose: - -```bash -docker-compose up -d -``` - -- The API will be exposed on port 8000 -- HuggingFace models will be cached in the lemonade-cache volume -- LLaMA binaries are persisted in lemonade-llama volume - -Check that the server is running: - -```bash -docker logs -f lemonade-server -``` - -You should see: - -```bash -lemonade-server | Lemonade Server vx.x.x started on port 8000 -lemonade-server | Chat and manage models: http://localhost:8000 -``` - ---- - -### 4. Access the API - -Test the API: -```bash -curl http://localhost:8000/api/v1/models -``` - -You should get a response with available models. - -### 5. Load a Model - -You can use the gui on localhost:8000 or below command to load a model (e.g., Qwen 0.6B): - -```bash -curl -X POST http://localhost:8000/api/v1/load \ - -H "Content-Type: application/json" \ - -d '{"model_name": "Qwen3-0.6B-GGUF"}' -``` - -The server will: -- Auto-download the GGUF model from HuggingFace -- Install the backend -- Make the model ready for inference - -### 6. Make a Chat Request - -Once the model is loaded: - -```python -from openai import OpenAI - -client = OpenAI( - base_url="http://localhost:8000/api/v1", - api_key="lemonade" # required but unused -) - -completion = client.chat.completions.create( - model="Qwen3-0.6B-GGUF", - messages=[{"role": "user", "content": "Hello, Lemonade!"}] -) - -print(completion.choices[0].message.content) -``` - -### 7. Stopping the Server - -```bash -docker-compose down -``` - -- Keeps cached models and binaries in Docker volumes -- You can restart anytime with docker-compose up -d - -### 8. Troubleshooting - -Server not starting: Check logs with: - -```bash -docker logs lemonade-server -``` - -- Model download fails: Ensure /root/.cache/huggingface volume is writable -- Vulkan errors on CPU-only machine: The server will fallback to CPU backend automatically# Listenr — AMD ROCm fine-tuning image +# Listenr — AMD ROCm fine-tuning image # # Base: official AMD ROCm PyTorch image (Python 3.12, ROCm-aware PyTorch pre-built). # Pull: podman pull rocm/pytorch:latest From 6b1062c81547f7751f705b069e6aec0daa56d05c Mon Sep 17 00:00:00 2001 From: rebreda Date: Sat, 7 Mar 2026 07:23:44 -0500 Subject: [PATCH 06/11] Update docs and dependencies --- README.md | 191 ++++------------------------------------ docs/configuration.md | 120 +++++++++++++++++++++++++ docs/dataset.md | 94 ++++++++++++++++++++ docs/finetune-amd.md | 173 ++++++++++++++++++++++++++++++++++++ docs/recording.md | 95 ++++++++++++++++++++ docs/setup.md | 82 +++++++++++++++++ docs/troubleshooting.md | 105 ++++++++++++++++++++++ pyproject.toml | 1 + uv.lock | 166 ++++++++++++++++++++++++++++++++++ 9 files changed, 854 insertions(+), 173 deletions(-) create mode 100644 docs/configuration.md create mode 100644 docs/dataset.md create mode 100644 docs/finetune-amd.md create mode 100644 docs/recording.md create mode 100644 docs/setup.md create mode 100644 docs/troubleshooting.md diff --git a/README.md b/README.md index 279eda6..fad1d89 100644 --- a/README.md +++ b/README.md @@ -11,193 +11,39 @@ Listenr is a privacy-first tool for collecting real-world audio and high-quality - **Open models.** Uses Whisper.cpp for transcription and any GGUF-compatible LLM for post-processing correction. - **Automatic correction pipeline.** A local LLM cleans up punctuation, grammar, and homophones — producing a higher-quality training corpus than raw Whisper output alone. - **Real-world data.** Collects natural, conversational speech in realistic environments. -- **Dataset-ready output.** Every utterance is saved with its audio clip, a per-clip JSON, and appended to a single `manifest.jsonl`. One command builds train/dev/test splits. +- **Dataset-ready output.** Every utterance is saved with its audio clip and appended to a single `manifest.jsonl`. One command builds train/dev/test splits. ## How It Works -1. **Capture.** `listenr` streams your microphone to Lemonade's `/realtime` WebSocket in ~85 ms chunks. Audio is captured at the device's native rate and resampled to 16 kHz before sending. -2. **VAD.** Lemonade's built-in server-side voice activity detection segments speech boundaries automatically. -3. **Transcribe.** Lemonade runs Whisper.cpp on each speech segment and streams back interim and final transcripts. -4. **Correct (optional).** The final transcript is sent to a local LLM via Lemonade's chat completions API. The LLM returns a cleaned transcript, an `is_improved` flag, and content `categories`. -5. **Save.** Each utterance is saved as a `.wav` clip and appended to `manifest.jsonl`. -6. **Build dataset.** `build_dataset.py` reads the manifest and writes train/dev/test CSV splits. +1. **Capture** — `listenr` streams your microphone to Lemonade's `/realtime` WebSocket in ~85 ms chunks, resampled to 16 kHz. +2. **VAD** — Lemonade's built-in voice activity detection segments speech boundaries automatically. +3. **Transcribe** — Lemonade runs Whisper.cpp on each segment and streams back transcripts. +4. **Correct (optional)** — a local LLM cleans the transcript and tags content categories. +5. **Save** — each utterance is saved as a `.wav` clip and a line in `manifest.jsonl`. +6. **Build dataset** — `listenr-build-dataset` writes train/dev/test splits from the manifest. - - -## Requirements - -- [Lemonade Server](https://lemonade-server.ai) running on `localhost:8000` -- Python 3.13+ with `uv` (recommended) or `pip` -- A microphone accessible via PipeWire or ALSA - -## Installation +## Quick Start ```bash git clone https://github.com/Rebreda/listenr cd listenr uv pip install -e . -``` - -Then run commands via `uv run` (no activation needed): - -```bash +lemonade-server serve # in another terminal uv run listenr ``` -Or activate the venv once per session: +See [docs/setup.md](docs/setup.md) for full installation instructions. -```bash -source .venv/bin/activate -listenr -``` +## Documentation -## Start Lemonade Server - -```bash -lemonade-server serve -``` - -Listenr will automatically call `POST /api/v1/load` on startup to load the configured models. On first use, Lemonade will download them. - -## Usage - -### CLI — Real-Time Microphone Capture - -```bash -# Record and save everything (default) -uv run listenr - -# Don't save to disk — just print transcriptions -uv run listenr --no-save - -# Also print the raw Whisper output before LLM correction -uv run listenr --show-raw - -# Verbose debug output (WebSocket messages, mic RMS, etc.) -uv run listenr --debug -``` - -Example output: - -``` -🎤 Listenr CLI — streaming to Lemonade - Model : Whisper-Large-v3-Turbo - WS URL : ws://localhost:9000/realtime?model=Whisper-Large-v3-Turbo - LLM : enabled (gpt-oss-20b-mxfp4-GGUF) - Save : yes → ~/.listenr/audio_clips - Press Ctrl+C to stop. - - [ASR] I'm going to the store to buy some milk. [dictation] - [SAVED] ~/.listenr/audio_clips/audio/2026-02-28/clip_2026-02-28_abc123.wav (2.4s) -``` - -Press **Ctrl+C** to stop. Listenr will unload all models from Lemonade before exiting. - -### Build a Dataset - -After collecting recordings, generate train/dev/test splits from `manifest.jsonl`: - -```bash -# Default: 80/10/10 CSV splits in ~/listenr_dataset/ -uv run listenr-build-dataset - -# Custom output directory and split ratio -uv run listenr-build-dataset --output ~/my_dataset --split 90/5/5 - -# Exclude very short clips -uv run listenr-build-dataset --min-duration 1.0 - -# HuggingFace datasets format -uv run listenr-build-dataset --format hf - -# Preview stats without writing files -uv run listenr-build-dataset --dry-run -``` - -Output CSV columns: `uuid`, `split`, `audio_path`, `raw_transcription`, `corrected_transcription`, `is_improved`, `categories`, `duration_s`, `sample_rate`, `whisper_model`, `llm_model`, `timestamp`. - -### Batch Transcription - -Transcribe a single audio file: - -```bash -python -m listenr.unified_asr --audio path/to/audio.wav --whisper-model Whisper-Large-v3-Turbo - -# With LLM correction -python -m listenr.unified_asr --llm --audio path/to/audio.wav -``` - -## Configuration - -Config is created with defaults at `~/.config/listenr/config.ini` on first run. - - -### Finding your input device - -```bash -python -c "import sounddevice as sd; [print(f'{i}: {d[\"name\"]}') for i, d in enumerate(sd.query_devices()) if d['max_input_channels'] > 0]" -``` - -Set `input_device` to the device name (partial match works) or its index number. - -### VAD Tuning - -| Goal | Setting | +| Guide | Description | |---|---| -| Shorter segments | Lower `silence_duration_ms` (e.g. `500`) | -| Avoid cutting off speech | Raise `silence_duration_ms` (e.g. `1200`) | -| Ignore background noise | Raise `threshold` (e.g. `0.05`) | -| Capture quiet speech | Lower `threshold` (e.g. `0.005`) | - - -### manifest.jsonl - -One JSON object per line — append-only, easy to query: - -```bash -# All improved clips -jq 'select(.is_improved == true)' ~/.listenr/audio_clips/manifest.jsonl - -# Clips tagged as commands -jq 'select(.categories[] == "command")' ~/.listenr/audio_clips/manifest.jsonl - -# Load into pandas -python -c "import pandas as pd; df = pd.read_json('~/.listenr/audio_clips/manifest.jsonl', lines=True); print(df.head())" -``` - -### manifest.jsonl - -**No transcriptions appear / `[SAVE SKIPPED] pcm_buffer is empty`** -- Check that Lemonade is running: `curl http://localhost:8000/api/v1/health` -- Run with `--debug` to see mic RMS values and WebSocket messages -- If RMS stays near `0.000`, your `input_device` is wrong — list devices and update config (see above) -- Lower `threshold` in `[VAD]` if your mic is quiet - -**LLM correction not working / model answers the transcription instead of fixing it** -- Confirm `LLM.enabled = true` and the model name matches one loaded in Lemonade -- Check `curl http://localhost:8000/api/v1/models` to see loaded models -- LLM errors are non-fatal — the raw transcript is saved regardless - -**`Could not discover Lemonade websocket port`** -Lemonade is not running or not reachable on port 8000. Run `lemonade-server serve` first. - -**Too many / too few segments** -Adjust `[VAD] silence_duration_ms` and `threshold` in your config. - -## Available Models (via Lemonade) - -| Model | Type | Notes | -|---|---|---| -| `Whisper-Base` | ASR | Fast, lower accuracy | -| `Whisper-Large-v3-Turbo` | ASR | Best accuracy | -| `gpt-oss-20b-mxfp4-GGUF` | LLM | Good correction quality | -| `Gemma-3-4b-it-GGUF` | LLM | Lighter alternative | -| `DeepSeek-Qwen3-8B-GGUF` | LLM | Lighter alternative | - -List all models available on your Lemonade instance: -```bash -curl -s http://localhost:8000/api/v1/models | python3 -c "import sys,json; [print(m['id']) for m in json.load(sys.stdin)['data']]" -``` +| [docs/setup.md](docs/setup.md) | Installation, Lemonade Server, microphone setup | +| [docs/configuration.md](docs/configuration.md) | Full `config.ini` reference, VAD tuning, available models | +| [docs/recording.md](docs/recording.md) | CLI usage, how recording works, batch transcription | +| [docs/dataset.md](docs/dataset.md) | Building train/dev/test splits, CSV and HF formats | +| [docs/finetune-amd.md](docs/finetune-amd.md) | Fine-tuning Whisper on AMD GPU via ROCm + Podman | +| [docs/troubleshooting.md](docs/troubleshooting.md) | Common errors and fixes | ## License @@ -208,4 +54,3 @@ Mozilla Public License Version 2.0 — see `LICENSE`. - [Lemonade Server](https://lemonade-server.ai) — unified local inference API - [whisper.cpp](https://github.com/ggerganov/whisper.cpp) — fast local ASR - [llama.cpp](https://github.com/ggerganov/llama.cpp) — fast local LLMs - diff --git a/docs/configuration.md b/docs/configuration.md new file mode 100644 index 0000000..41d880b --- /dev/null +++ b/docs/configuration.md @@ -0,0 +1,120 @@ +# Configuration + +Config is created with defaults at `~/.config/listenr/config.ini` on first run. +Edit it directly — changes take effect on the next `listenr` invocation. + +--- + +## Full reference + +```ini +[Lemonade] +api_base = http://localhost:8000/api/v1 + +[Whisper] +model = Whisper-Tiny + +[Audio] +sample_rate = 48000 +channels = 1 +blocksize = 4096 +# Device name (partial match) or index number. Leave blank for system default. +input_device = pipewire + +[VAD] +threshold = 0.05 +silence_duration_ms = 800 +prefix_padding_ms = 250 + +[LLM] +enabled = true +model = gpt-oss-20b-mxfp4-GGUF +api_base = http://localhost:8000/api/v1 +temperature = 0.3 +max_tokens = 1500 +timeout = 30 +context_window = 10 + +[Storage] +audio_clips_path = ~/.listenr/audio_clips +audio_clips_enabled = true +retention_days = 90 +max_storage_gb = 10.0 + +[Dataset] +output_path = ~/listenr_dataset +split = 80/10/10 +min_duration = 0.3 +min_chars = 2 +seed = 42 +format = csv +strip_tags = true + +[Finetune] +base_model = openai/whisper-small +language = english +task = transcribe +lora_r = 8 +lora_alpha = 32 +lora_dropout = 0.1 +lora_target_modules = q_proj,v_proj +freeze_encoder = true +learning_rate = 0.0001 +warmup_steps = 100 +max_steps = 2000 +batch_size = 8 +grad_accum_steps = 2 +fp16 = false +bf16 = false +output_dir = ~/listenr_finetune +eval_steps = 200 +save_steps = 400 +generation_max_length = 128 + +[Output] +# Optional: write all transcriptions to a file as well as stdout +file = +llm_file = +line_format = [{timestamp}] {text} +timestamp_format = %Y-%m-%d %H:%M:%S +show_raw = false + +[Logging] +level = INFO +file = +``` + +--- + +## VAD tuning + +Voice Activity Detection controls how speech segments are carved out of the +audio stream. Adjust these two settings in `[VAD]`: + +| Goal | Setting | +|---|---| +| Shorter segments / snappier cuts | Lower `silence_duration_ms` (e.g. `500`) | +| Avoid cutting off speech | Raise `silence_duration_ms` (e.g. `1200`) | +| Ignore background noise | Raise `threshold` (e.g. `0.05`) | +| Capture quiet speech | Lower `threshold` (e.g. `0.005`) | + +--- + +## Available Lemonade models + +List all models currently loaded on your Lemonade instance: + +```bash +curl -s http://localhost:8000/api/v1/models | \ + python3 -c "import sys,json; [print(m['id']) for m in json.load(sys.stdin)['data']]" +``` + +Common options: + +| Model | Type | Notes | +|---|---|---| +| `Whisper-Base` | ASR | Fast, lower accuracy | +| `Whisper-Large-v3-Turbo` | ASR | Best accuracy | +| `gpt-oss-20b-mxfp4-GGUF` | LLM | Good correction quality | +| `Gemma-3-4b-it-GGUF` | LLM | Lighter alternative | +| `DeepSeek-Qwen3-8B-GGUF` | LLM | Lighter alternative | diff --git a/docs/dataset.md b/docs/dataset.md new file mode 100644 index 0000000..64e90c2 --- /dev/null +++ b/docs/dataset.md @@ -0,0 +1,94 @@ +# Building a Dataset + +`listenr-build-dataset` reads `manifest.jsonl` and writes train/dev/test splits +in CSV and/or HuggingFace datasets format, ready to pass to `listenr-finetune`. + +--- + +## Usage + +```bash +# Default: 80/10/10 CSV splits → ~/listenr_dataset/ +uv run listenr-build-dataset + +# Custom output directory and split ratio +uv run listenr-build-dataset --output ~/my_dataset --split 90/5/5 + +# Exclude very short or sparse clips +uv run listenr-build-dataset --min-duration 1.0 --min-chars 10 + +# HuggingFace datasets format (required for listenr-finetune) +uv run listenr-build-dataset --format hf + +# Both CSV and HF at once +uv run listenr-build-dataset --format both + +# Preview stats without writing anything +uv run listenr-build-dataset --dry-run +``` + +--- + +## All options + +| Flag | Default | Description | +|---|---|---| +| `--manifest PATH` | `~/.listenr/audio_clips/manifest.jsonl` | Input manifest file | +| `--output DIR` | `~/listenr_dataset` | Output directory | +| `--split TRAIN/DEV/TEST` | `80/10/10` | Split percentages | +| `--min-duration SECS` | `0.3` | Minimum clip duration | +| `--min-chars N` | `2` | Minimum transcript length (non-whitespace chars) | +| `--format csv\|hf\|both` | `csv` | Output format | +| `--seed N` | `42` | Random seed for reproducible splits | +| `--no-strip-tags` | off | Keep noise tags like `(music)` in transcriptions | +| `--remap-audio-prefix OLD:NEW` | — | Rewrite audio path prefix (useful in containers) | +| `--dry-run` | off | Print stats and exit without writing files | + +--- + +## Output: CSV + +Three files in `--output`: + +``` +train.csv +dev.csv +test.csv +``` + +Columns: `uuid`, `split`, `audio_path`, `raw_transcription`, +`corrected_transcription`, `is_improved`, `categories`, `duration_s`, +`sample_rate`, `whisper_model`, `llm_model`, `timestamp`. + +--- + +## Output: HuggingFace datasets + +Creates an `hf_dataset/` directory loadable with: + +```python +from datasets import load_from_disk +ds = load_from_disk("~/listenr_dataset/hf_dataset") +print(ds) +``` + +The `Audio` feature is loaded lazily — audio files are read from disk only +when the batch is accessed. Pass this directory directly to `listenr-finetune`. + +--- + +## Path remapping (container use) + +`manifest.jsonl` stores absolute host paths. When running inside a container +where your data is mounted at a different location, use `--remap-audio-prefix` +to fix them at read time: + +```bash +listenr-build-dataset \ + --manifest /data/listenr/audio_clips/manifest.jsonl \ + --output /data/dataset \ + --format hf \ + --remap-audio-prefix /home/you/.listenr/audio_clips:/data/listenr/audio_clips +``` + +The original `manifest.jsonl` is never modified. diff --git a/docs/finetune-amd.md b/docs/finetune-amd.md new file mode 100644 index 0000000..373ea07 --- /dev/null +++ b/docs/finetune-amd.md @@ -0,0 +1,173 @@ +# Fine-tuning on AMD GPU (ROCm) + +This guide covers running `listenr-finetune` inside the official AMD ROCm +PyTorch container using Podman. The fine-tuning code works on the host too if +you already have ROCm PyTorch installed — the container is just the easiest way +to get a working GPU environment. + +> **Note:** Real-time microphone capture (`listenr`) does not work inside the +> container. Record audio on the host first, build a dataset, then fine-tune +> here. + +--- + +## Prerequisites + +- AMD GPU with ROCm driver installed on the host +- Podman installed (`podman --version`) +- At least ~50 GB free disk space (model cache + audio data + checkpoints) +- Recordings collected on the host via `listenr` (see [recording.md](recording.md)) + +--- + +## 1. Pull the image + +```bash +podman pull rocm/pytorch:latest +``` + +--- + +## 2. Build the dataset (on host or in container) + +**On the host** (simplest, if you have the finetune deps installed): + +```bash +uv run listenr-build-dataset --format hf +# Output: ~/listenr_dataset/hf_dataset +``` + +**Inside the container** (if you only want the finetune stack there): + +```bash +podman run -it \ + --cap-add=SYS_PTRACE \ + --security-opt seccomp=unconfined \ + --ipc=host \ + -v ~/.listenr:/data/listenr \ + -v ~/listenr_dataset:/data/dataset \ + -w /app \ + listenr-rocm \ + listenr-build-dataset \ + --manifest /data/listenr/audio_clips/manifest.jsonl \ + --output /data/dataset \ + --format hf \ + --remap-audio-prefix /home/$(whoami)/.listenr/audio_clips:/data/listenr/audio_clips +``` + +--- + +## 3. Build the listenr image + +From the repo root: + +```bash +podman build -t listenr-rocm . +``` + +This installs `listenr` with all `[finetune]` extras on top of `rocm/pytorch:latest`. + +--- + +## 4. Run fine-tuning + +```bash +podman run -it \ + --cap-add=SYS_PTRACE \ + --security-opt seccomp=unconfined \ + --device=/dev/kfd \ + --device=/dev/dri \ + --group-add video \ + --ipc=host \ + -e HIP_VISIBLE_DEVICES=0 \ + -v ~/listenr_dataset:/data/dataset \ + -v ~/listenr_finetune:/data/adapter \ + -v ~/.cache/huggingface:/root/.cache/huggingface \ + -w /app \ + listenr-rocm \ + listenr-finetune \ + --dataset /data/dataset/hf_dataset \ + --output /data/adapter \ + --bf16 +``` + +The adapter checkpoint is written to `~/listenr_finetune` on the host via +the bind mount. + +--- + +## 5. Using podman compose + +Copy `.env.example` to `.env` and edit for your system, then: + +```bash +# Build dataset +podman compose run --rm build-dataset + +# Fine-tune +podman compose run --rm finetune + +# Pass extra args +podman compose run --rm finetune --max-steps 500 --lora-r 16 +``` + +--- + +## Common flags for listenr-finetune + +| Flag | Default | Description | +|---|---|---| +| `--dataset DIR` | `~/listenr_dataset/hf_dataset` | HF dataset directory | +| `--output DIR` | `~/listenr_finetune` | Adapter checkpoint output | +| `--base-model ID` | `openai/whisper-small` | HuggingFace model to fine-tune | +| `--bf16` | off | bf16 mixed precision — **use this on AMD** | +| `--fp16` | off | fp16 mixed precision — CUDA only, not recommended for AMD | +| `--max-steps N` | `2000` | Total training steps | +| `--batch-size N` | `8` | Per-device batch size | +| `--lora-r N` | `8` | LoRA rank | +| `--language LANG` | `english` | Target language | +| `--dry-run` | off | Load data + model, print stats, then exit | + +--- + +## GPU selection + +If you have multiple GPUs, pin to one to avoid imbalance issues: + +```bash +-e HIP_VISIBLE_DEVICES=0 # use GPU 0 only +``` + +Check GPU IDs and memory: + +```bash +rocm-smi +``` + +--- + +## If your GPU's gfx version is unsupported by ROCm + +Some GPUs (especially consumer RDNA2/RDNA3 cards) need the override: + +```bash +-e HSA_OVERRIDE_GFX_VERSION=10.3.0 # RX 6000 series (RDNA2) +-e HSA_OVERRIDE_GFX_VERSION=11.0.0 # RX 7000 series (RDNA3) +``` + +Or set it in your `.env` file (it's read automatically by `podman compose`). + +--- + +## Loading the adapter + +The fine-tuned LoRA adapter is saved to `~/listenr_finetune`. Load it with: + +```python +from peft import PeftModel +from transformers import WhisperForConditionalGeneration + +base = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") +model = PeftModel.from_pretrained(base, "~/listenr_finetune") +model = model.merge_and_unload() # optional: merge for inference +``` diff --git a/docs/recording.md b/docs/recording.md new file mode 100644 index 0000000..c770c0e --- /dev/null +++ b/docs/recording.md @@ -0,0 +1,95 @@ +# Recording + +`listenr` streams your microphone to Lemonade in real time, receives Whisper +transcriptions, optionally corrects them with a local LLM, and saves each +utterance as a `.wav` clip. + +--- + +## Basic usage + +```bash +# Record and save everything (default) +uv run listenr + +# Print transcriptions only — nothing saved to disk +uv run listenr --no-save + +# Also print the raw Whisper output before LLM correction +uv run listenr --show-raw + +# Verbose debug output (WebSocket messages, mic RMS, etc.) +uv run listenr --debug +``` + +Press **Ctrl+C** to stop. Listenr will unload all models from Lemonade before exiting. + +--- + +## Example output + +``` +🎤 Listenr CLI — streaming to Lemonade + Model : Whisper-Large-v3-Turbo + WS URL : ws://localhost:9000/realtime?model=Whisper-Large-v3-Turbo + LLM : enabled (gpt-oss-20b-mxfp4-GGUF) + Save : yes → ~/.listenr/audio_clips + Press Ctrl+C to stop. + + [ASR] I'm going to the store to buy some milk. [dictation] + [SAVED] ~/.listenr/audio_clips/audio/2026-02-28/clip_2026-02-28_abc123.wav (2.4s) +``` + +--- + +## How it works + +1. **Capture** — audio is streamed from your microphone at the device's native + sample rate and resampled to 16 kHz PCM-16 before sending. +2. **VAD** — Lemonade's server-side voice activity detection segments speech + boundaries automatically. +3. **Transcribe** — Lemonade runs Whisper.cpp on each speech segment and streams + back interim and final transcripts. +4. **Correct (optional)** — the final transcript is sent to a local LLM. The + LLM returns a cleaned transcript, an `is_improved` flag, and content + `categories`. LLM errors are non-fatal — the raw transcript is saved regardless. +5. **Save** — each utterance is saved as: + - `~/.listenr/audio_clips/audio//clip_.wav` + - a line appended to `~/.listenr/audio_clips/manifest.jsonl` + +--- + +## Batch / file transcription + +Transcribe an existing audio file without the microphone: + +```bash +python -m listenr.unified_asr \ + --audio path/to/audio.wav \ + --whisper-model Whisper-Large-v3-Turbo + +# With LLM correction +python -m listenr.unified_asr --llm --audio path/to/audio.wav +``` + +--- + +## manifest.jsonl + +Every recorded utterance is appended to `~/.listenr/audio_clips/manifest.jsonl` +as a single JSON object per line. It is append-only and easy to inspect: + +```bash +# All LLM-improved clips +jq 'select(.is_improved == true)' ~/.listenr/audio_clips/manifest.jsonl + +# Clips tagged as commands +jq 'select(.categories[] == "command")' ~/.listenr/audio_clips/manifest.jsonl + +# Load into pandas +python -c " +import pandas as pd +df = pd.read_json('~/.listenr/audio_clips/manifest.jsonl', lines=True) +print(df.head()) +" +``` diff --git a/docs/setup.md b/docs/setup.md new file mode 100644 index 0000000..69bcf4e --- /dev/null +++ b/docs/setup.md @@ -0,0 +1,82 @@ +# Setup + +## Requirements + +| Requirement | Notes | +|---|---| +| Python 3.13+ | via `uv`, `pyenv`, or system package manager | +| [Lemonade Server](https://lemonade-server.ai) | runs locally on `localhost:8000` | +| Microphone | accessible via PipeWire or ALSA (Linux) | +| `uv` | recommended Python package manager | + +--- + +## Install + +```bash +git clone https://github.com/Rebreda/listenr +cd listenr +uv pip install -e . +``` + +### Optional: fine-tuning dependencies + +Only needed if you plan to run `listenr-finetune`. Requires PyTorch. + +```bash +uv pip install -e ".[finetune]" +``` + +> For AMD GPU fine-tuning, use the ROCm container instead — see [finetune-amd.md](finetune-amd.md). + +--- + +## Run without activating the venv + +```bash +uv run listenr +uv run listenr-build-dataset +uv run listenr-finetune +``` + +## Or activate once per session + +```bash +source .venv/bin/activate +listenr +``` + +--- + +## Start Lemonade Server + +Listenr talks to Lemonade over HTTP/WebSocket — it must be running before you start `listenr`. + +```bash +lemonade-server serve +``` + +On first run, Lemonade will download the configured models. Listenr calls +`POST /api/v1/load` automatically on startup. + +Check it is reachable: + +```bash +curl http://localhost:8000/api/v1/health +``` + +--- + +## Finding your microphone device + +```bash +python -c " +import sounddevice as sd +for i, d in enumerate(sd.query_devices()): + if d['max_input_channels'] > 0: + print(f\"{i}: {d['name']}\") +" +``` + +Set `input_device` in `~/.config/listenr/config.ini` to the device name (partial +match works) or its index number. See [configuration.md](configuration.md). diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md new file mode 100644 index 0000000..762eff4 --- /dev/null +++ b/docs/troubleshooting.md @@ -0,0 +1,105 @@ +# Troubleshooting + +--- + +## Recording / CLI + +### No transcriptions appear / `[SAVE SKIPPED] pcm_buffer is empty` + +- Check Lemonade is running: `curl http://localhost:8000/api/v1/health` +- Run with `--debug` to see mic RMS values and WebSocket messages +- If RMS stays near `0.000`, your `input_device` setting is wrong — list + devices and update config (see [setup.md](setup.md)) +- Lower `threshold` in `[VAD]` if your mic is quiet + +### LLM correction not working / model answers the prompt instead of fixing it + +- Confirm `LLM.enabled = true` and the model name matches one loaded in Lemonade +- Check `curl http://localhost:8000/api/v1/models` to see loaded models +- LLM errors are non-fatal — the raw transcript is saved regardless + +### `Could not discover Lemonade websocket port` + +Lemonade is not running or not reachable on `localhost:8000`. +Run `lemonade-server serve` and wait for it to finish starting. + +### Too many / too few segments + +Adjust `[VAD] silence_duration_ms` and `threshold` in your config. +See [configuration.md](configuration.md) for guidance. + +--- + +## Dataset building + +### `Valid entries: 0, skipped: N` + +All entries failed validation. The most common causes: + +1. **Audio files not found** — `manifest.jsonl` stores absolute host paths. + Inside a container, use `--remap-audio-prefix OLD:NEW` to rewrite them: + ```bash + --remap-audio-prefix /home/you/.listenr/audio_clips:/data/listenr/audio_clips + ``` +2. **Clips too short** — lower `--min-duration` (default: `0.3s`) +3. **Transcripts too short** — lower `--min-chars` (default: `2`) + +Run with `--dry-run` to see counts before committing any writes. + +### Only train/test splits, no dev split + +Not enough recordings. With fewer than ~10 clips, the 80/10/10 split rounds to +zero dev entries. Collect more data, or adjust `--split`, e.g. `--split 80/20/0`. + +--- + +## Fine-tuning (AMD / ROCm) + +### Container exits with code 139 (segfault) + +Usually caused by GPU imbalance on a multi-GPU system. Restrict to one GPU: + +```bash +-e HIP_VISIBLE_DEVICES=0 +``` + +### `HSA_STATUS_ERROR_INVALID_ISA` or GPU not detected in container + +Your GPU's gfx version may not be natively supported by the ROCm version in the +image. Override it: + +```bash +-e HSA_OVERRIDE_GFX_VERSION=10.3.0 # RX 6000 series (RDNA2) +-e HSA_OVERRIDE_GFX_VERSION=11.0.0 # RX 7000 series (RDNA3) +``` + +Check your GPU model: `rocm-smi --showproductname` + +### `cannot set shmsize when running in the host IPC Namespace` + +Cannot combine `--shm-size` with `--ipc=host`. Drop `--shm-size` — when +`--ipc=host` is set, the container shares the host's `/dev/shm` directly. + +### `command not found: docker` — you have Podman + +Fedora and other distributions ship `podman` instead of `docker`. Replace +`docker` with `podman` in all commands. They are CLI-compatible for `run` and +`build`. For compose, use `podman compose` (requires `podman-compose` package). + +### Out of GPU memory (OOM) + +Reduce memory usage: + +```bash +listenr-finetune --batch-size 2 --grad-accum 8 --bf16 +``` + +The effective batch size is `batch_size × grad_accum`. Keeping that product the +same (e.g. `8×2 = 16` → `2×8 = 16`) preserves training dynamics. + +### `pip install` ignores `requires-python` warning + +The `rocm/pytorch` image ships Python 3.12; listenr requires `>=3.13`. The +codebase uses no 3.13-specific syntax — the `--ignore-requires-python` flag +is safe here. If a future change introduces 3.13-only syntax, update the +`FROM` line in `Dockerfile` to a newer base image. diff --git a/pyproject.toml b/pyproject.toml index fc0ce3c..bd50f4b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ finetune = [ "datasets", "evaluate", "jiwer", + "tensorboard", ] [project.scripts] diff --git a/uv.lock b/uv.lock index a413fa1..9f413cd 100644 --- a/uv.lock +++ b/uv.lock @@ -10,6 +10,15 @@ resolution-markers = [ "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] +[[package]] +name = "absl-py" +version = "2.4.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" } +wheels = [ + { url = 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pytorch version, update docs --- .env.example | 6 +++-- Dockerfile | 53 +++++++++++++++++++++++++++-------------- docker-compose.yml | 21 ++++++++++------ docs/finetune-amd.md | 34 ++++++++++++++++++++++---- docs/troubleshooting.md | 19 ++++++++++++++- 5 files changed, 100 insertions(+), 33 deletions(-) diff --git a/.env.example b/.env.example index 7a44ca7..e553269 100644 --- a/.env.example +++ b/.env.example @@ -8,8 +8,10 @@ # Set to "0,1" to use both GPUs if they are the same model. HIP_VISIBLE_DEVICES=0 -# Uncomment and set if your GPU's gfx version isn't natively supported. -# Check with: rocm-smi --showproductname +# Set ONLY if your GPU's gfx version isn't natively supported by ROCm. +# WARNING: Do NOT set this to an empty string — an empty string is not the +# same as unset and will cause ROCm to fail. Only uncomment if needed. +# Check your GPU: rocm-smi --showproductname # RX 6000 series (RDNA2): 10.3.0 # RX 7000 series (RDNA3): 11.0.0 # HSA_OVERRIDE_GFX_VERSION=10.3.0 diff --git a/Dockerfile b/Dockerfile index c05e632..fc80a3c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,25 +1,31 @@ # Listenr — AMD ROCm fine-tuning image # -# Base: official AMD ROCm PyTorch image (Python 3.12, ROCm-aware PyTorch pre-built). -# Pull: podman pull rocm/pytorch:latest -# Build: podman build -t listenr-rocm . -# Run: podman compose run --rm finetune +# Base: official AMD-tested ROCm 7.2 + PyTorch 2.9.1 image (Python 3.12). +# Ref: https://rocm.docs.amd.com/en/latest/how_to/pytorch_install/pytorch_install.html # -# NOTE: The rocm/pytorch base ships Python 3.12. listenr's pyproject.toml -# requires Python >=3.13. We install with --ignore-requires-python -# because the codebase uses no 3.13-only syntax — the constraint exists -# to encourage modern local installs, not to block Docker usage. +# Pull: podman pull rocm/pytorch:rocm7.2_ubuntu24.04_py3.12_pytorch_release_2.9.1 +# Build: podman build -t listenr-rocm . +# Run: see docs/finetune-amd.md # # NOTE: sounddevice (microphone capture) will not work inside this container. # This image is intended for fine-tuning only (listenr-finetune / # listenr-build-dataset), not real-time audio capture. +# +# NOTE: listenr requires Python >=3.13 for local installs; this image uses +# Python 3.12 (the AMD-tested version). --ignore-requires-python is safe +# here — the codebase uses no 3.13-only syntax. -FROM rocm/pytorch:latest +FROM rocm/pytorch:rocm7.2_ubuntu24.04_py3.12_pytorch_release_2.9.1 # ── system packages ────────────────────────────────────────────────────────── # libsndfile1 : required by soundfile (audio I/O in finetune data pipeline) # ffmpeg : optional but useful for converting audio files -RUN apt-get update && apt-get install -y --no-install-recommends \ +# +# IMPORTANT: We must not upgrade libdrm, mesa, or any ROCm library — doing so +# breaks the GPU stack that ships with the base image. Use --no-upgrade to +# install only what is missing (both packages are typically absent from the +# base image but their deps like libdrm are already present). +RUN apt-get update && apt-get install -y --no-install-recommends --no-upgrade \ libsndfile1 \ ffmpeg \ && rm -rf /var/lib/apt/lists/* @@ -28,18 +34,29 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ WORKDIR /app COPY . /app -# Install core + finetune extras. -# --ignore-requires-python: base image has Python 3.10; constraint is 3.13+ -# but no 3.13-specific syntax is used in this codebase. +# Freeze the ROCm-aware torch/torchvision/torchaudio/triton that ship in the +# base image before installing finetune extras. Without this, pip resolves +# transformers' torch dependency and pulls a CPU-only build from PyPI. +# pip show works regardless of whether torch was installed via URL or wheel. +RUN pip show torch torchvision torchaudio triton 2>/dev/null \ + | awk '/^Name:/{name=$2} /^Version:/{print name "==" $2}' \ + > /tmp/torch-constraints.txt \ + && cat /tmp/torch-constraints.txt + +# Install core + finetune extras, pinning torch to the ROCm version above. +# --ignore-requires-python: base image is Python 3.12; constraint is >=3.13. RUN pip install --no-cache-dir \ --ignore-requires-python \ + --constraint /tmp/torch-constraints.txt \ -e ".[finetune]" # ── runtime defaults ───────────────────────────────────────────────────────── -# Override HSA_OVERRIDE_GFX_VERSION if your GPU reports an unsupported gfx. -# Set HIP_VISIBLE_DEVICES=0 to restrict to a single GPU (avoids imbalance -# warnings and segfaults on multi-GPU systems). -ENV HSA_OVERRIDE_GFX_VERSION="" \ - HIP_VISIBLE_DEVICES="0" +# Pin to GPU 0 by default to avoid imbalance crashes on multi-GPU systems. +# Override at runtime: -e HIP_VISIBLE_DEVICES=0,1 +# +# Do NOT set HSA_OVERRIDE_GFX_VERSION here — an empty string is not the same +# as unset and causes ROCm to fail. Set it at runtime only if your GPU needs +# it (e.g. -e HSA_OVERRIDE_GFX_VERSION=10.3.0 for RX 6000 series). +ENV HIP_VISIBLE_DEVICES="0" CMD ["listenr-finetune", "--help"] diff --git a/docker-compose.yml b/docker-compose.yml index 3bac47a..e442760 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -22,18 +22,25 @@ services: - seccomp=unconfined ipc: host devices: - - /dev/kfd # AMD GPU compute - - /dev/dri # AMD GPU render + - /dev/kfd + - /dev/dri/card0 + - /dev/dri/card1 + - /dev/dri/renderD128 + - /dev/dri/renderD129 + # keep-groups passes the host user's actual supplementary GIDs (render, video) + # into the container, bypassing the GID name→number mismatch between the + # Ubuntu-based image (render=991, video=44) and the host (render=105, video=39). group_add: - - video - - render + - keep-groups environment: # Pin to one GPU to avoid imbalance warnings/segfaults on multi-GPU systems. # Set to "0,1" to use both GPUs if they are matched. HIP_VISIBLE_DEVICES: "${HIP_VISIBLE_DEVICES:-0}" - # Override if your GPU's gfx version isn't natively supported by ROCm. - # Leave blank to let ROCm auto-detect. - HSA_OVERRIDE_GFX_VERSION: "${HSA_OVERRIDE_GFX_VERSION:-}" + # Only set HSA_OVERRIDE_GFX_VERSION if your GPU needs it. + # An empty string is NOT the same as unset — it causes ROCm to fail. + # Uncomment in .env only if needed: + # HSA_OVERRIDE_GFX_VERSION=10.3.0 (RX 6000 / RDNA2) + # HSA_OVERRIDE_GFX_VERSION=11.0.0 (RX 7000 / RDNA3) volumes: # HF dataset produced by build-dataset (DATASET_OUTPUT default: ~/listenr_dataset) - "${LISTENR_DATASET:-${HOME}/listenr_dataset}:/data/dataset" diff --git a/docs/finetune-amd.md b/docs/finetune-amd.md index 373ea07..d915492 100644 --- a/docs/finetune-amd.md +++ b/docs/finetune-amd.md @@ -22,10 +22,15 @@ to get a working GPU environment. ## 1. Pull the image +Use the specific AMD-tested stable tag rather than `latest`: + ```bash -podman pull rocm/pytorch:latest +podman pull rocm/pytorch:rocm7.2_ubuntu24.04_py3.12_pytorch_release_2.9.1 ``` +> The `latest` tag can point to an untested or preview build. The versioned +> tag is what AMD validates and documents. + --- ## 2. Build the dataset (on host or in container) @@ -44,6 +49,12 @@ podman run -it \ --cap-add=SYS_PTRACE \ --security-opt seccomp=unconfined \ --ipc=host \ + --device=/dev/kfd \ + --device=/dev/dri/card0 \ + --device=/dev/dri/card1 \ + --device=/dev/dri/renderD128 \ + --device=/dev/dri/renderD129 \ + --group-add keep-groups \ -v ~/.listenr:/data/listenr \ -v ~/listenr_dataset:/data/dataset \ -w /app \ @@ -65,7 +76,10 @@ From the repo root: podman build -t listenr-rocm . ``` -This installs `listenr` with all `[finetune]` extras on top of `rocm/pytorch:latest`. +This installs `listenr` with all `[finetune]` extras on top of +`rocm/pytorch:rocm7.2_ubuntu24.04_py3.12_pytorch_release_2.9.1`. +The ROCm-aware PyTorch wheel is pinned during the build so `pip` cannot +replace it with a CPU-only build from PyPI. --- @@ -76,8 +90,11 @@ podman run -it \ --cap-add=SYS_PTRACE \ --security-opt seccomp=unconfined \ --device=/dev/kfd \ - --device=/dev/dri \ - --group-add video \ + --device=/dev/dri/card0 \ + --device=/dev/dri/card1 \ + --device=/dev/dri/renderD128 \ + --device=/dev/dri/renderD129 \ + --group-add keep-groups \ --ipc=host \ -e HIP_VISIBLE_DEVICES=0 \ -v ~/listenr_dataset:/data/dataset \ @@ -138,12 +155,19 @@ If you have multiple GPUs, pin to one to avoid imbalance issues: -e HIP_VISIBLE_DEVICES=0 # use GPU 0 only ``` -Check GPU IDs and memory: +Check GPU IDs, names, and memory: ```bash rocm-smi ``` +> **Note on `--group-add keep-groups`:** The `rocm/pytorch` image uses an +> Ubuntu base with different GIDs for the `render` and `video` groups than +> Fedora/RHEL hosts. Passing `--group-add render` resolves to the wrong GID +> inside the container, so the process can't access `/dev/kfd`. `keep-groups` +> passes the host user's actual numeric supplementary GIDs directly, +> bypassing the name→GID mismatch entirely. + --- ## If your GPU's gfx version is unsupported by ROCm diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md index 762eff4..e30831a 100644 --- a/docs/troubleshooting.md +++ b/docs/troubleshooting.md @@ -63,7 +63,24 @@ Usually caused by GPU imbalance on a multi-GPU system. Restrict to one GPU: -e HIP_VISIBLE_DEVICES=0 ``` -### `HSA_STATUS_ERROR_INVALID_ISA` or GPU not detected in container +### `torch.cuda.is_available()` returns False in built image but True in base image + +Two known causes: + +1. **`HSA_OVERRIDE_GFX_VERSION` set to empty string** — an empty string is not + the same as unset and causes ROCm to fail silently. Never set this variable + unless you have a specific gfx version to override; omit it entirely otherwise. + +2. **pip replaced the ROCm torch wheel with a CPU-only build** — installs of + `transformers` or other packages can pull a vanilla `torch` from PyPI as a + dependency, silently replacing the ROCm wheel. The `Dockerfile` guards + against this with a `pip freeze` constraint file before installing extras. + Verify with: `python3 -c "import torch; print(torch.version.hip)"` + — if this prints `None`, the ROCm wheel was replaced. + +3. **Using `rocm/pytorch:latest`** — the `latest` tag can point to a preview + or less-tested build. Use the specific stable tag: + `rocm/pytorch:rocm7.2_ubuntu24.04_py3.12_pytorch_release_2.9.1` Your GPU's gfx version may not be natively supported by the ROCm version in the image. Override it: From ad876f761c17036dd9fae8427dc91680b1783edc Mon Sep 17 00:00:00 2001 From: rebreda Date: Sat, 7 Mar 2026 08:38:48 -0500 Subject: [PATCH 08/11] Add missing tests and update config --- .env.example | 4 + Dockerfile | 7 +- docker-compose.yml | 3 + tests/test_build_dataset.py | 394 ++++++++++++++++++++++++++++++++++++ 4 files changed, 407 insertions(+), 1 deletion(-) create mode 100644 tests/test_build_dataset.py diff --git a/.env.example b/.env.example index e553269..77d01b2 100644 --- a/.env.example +++ b/.env.example @@ -8,6 +8,10 @@ # Set to "0,1" to use both GPUs if they are the same model. HIP_VISIBLE_DEVICES=0 +# Enables Flash Efficient and Memory Efficient attention on RDNA3+ GPUs (RX 7000 / 9000). +# Set to empty string to disable (PyTorch will log a warning and use the slow path). +TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 + # Set ONLY if your GPU's gfx version isn't natively supported by ROCm. # WARNING: Do NOT set this to an empty string — an empty string is not the # same as unset and will cause ROCm to fail. Only uncomment if needed. diff --git a/Dockerfile b/Dockerfile index fc80a3c..68d4c64 100644 --- a/Dockerfile +++ b/Dockerfile @@ -57,6 +57,11 @@ RUN pip install --no-cache-dir \ # Do NOT set HSA_OVERRIDE_GFX_VERSION here — an empty string is not the same # as unset and causes ROCm to fail. Set it at runtime only if your GPU needs # it (e.g. -e HSA_OVERRIDE_GFX_VERSION=10.3.0 for RX 6000 series). -ENV HIP_VISIBLE_DEVICES="0" +# +# TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL: enables Flash Efficient and +# Memory Efficient attention on newer AMD GPUs (RDNA 3 / RDNA 4). Without +# this, PyTorch logs a UserWarning and falls back to a slower implementation. +ENV HIP_VISIBLE_DEVICES="0" \ + TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL="1" CMD ["listenr-finetune", "--help"] diff --git a/docker-compose.yml b/docker-compose.yml index e442760..a1d95ba 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -36,6 +36,9 @@ services: # Pin to one GPU to avoid imbalance warnings/segfaults on multi-GPU systems. # Set to "0,1" to use both GPUs if they are matched. HIP_VISIBLE_DEVICES: "${HIP_VISIBLE_DEVICES:-0}" + # Enables Flash Efficient / Memory Efficient attention on RDNA3+. + # Set to "" to disable (falls back to slower implementation with a warning). + TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL: "${TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL:-1}" # Only set HSA_OVERRIDE_GFX_VERSION if your GPU needs it. # An empty string is NOT the same as unset — it causes ROCm to fail. # Uncomment in .env only if needed: diff --git a/tests/test_build_dataset.py b/tests/test_build_dataset.py new file mode 100644 index 0000000..f3f8fec --- /dev/null +++ b/tests/test_build_dataset.py @@ -0,0 +1,394 @@ +""" +Unit tests for build_dataset — load_manifest, validate_entry, parse_split, +assign_splits, write_csv, and the --remap-audio-prefix feature. + +Run with: python -m pytest tests/test_build_dataset.py +""" + +import csv +import json +import numpy as np +import soundfile as sf +import pytest +from pathlib import Path + +from listenr.build_dataset import ( + load_manifest, + validate_entry, + parse_split, + assign_splits, + write_csv, + CSV_COLUMNS, +) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _wav(path: Path, duration_s: float = 2.0, sample_rate: int = 16_000) -> Path: + """Write a silent WAV file and return its path.""" + samples = np.zeros(int(duration_s * sample_rate), dtype="float32") + sf.write(str(path), samples, sample_rate) + return path + + +def _record( + tmp_path: Path, + uuid: str = "abc123", + raw: str = "hello world", + corrected: str = "hello world", + duration_s: float = 2.0, + audio_name: str = "clip.wav", + create_file: bool = True, +) -> dict: + """Return a manifest record with an optional real WAV file.""" + audio_path = tmp_path / audio_name + if create_file: + _wav(audio_path, duration_s) + return { + "uuid": uuid, + "raw_transcription": raw, + "corrected_transcription": corrected, + "audio_path": str(audio_path), + "duration_s": duration_s, + "sample_rate": 16000, + "whisper_model": "whisper-base", + "llm_model": "gpt-oss", + "is_improved": "True", + "timestamp": "2025-01-01T00:00:00", + } + + +# --------------------------------------------------------------------------- +# load_manifest +# --------------------------------------------------------------------------- + +class TestLoadManifest: + def test_returns_empty_list_when_file_missing(self, tmp_path): + result = load_manifest(tmp_path / "no_such_file.jsonl") + assert result == [] + + def test_loads_valid_jsonl(self, tmp_path): + mf = tmp_path / "manifest.jsonl" + records = [{"uuid": "a"}, {"uuid": "b"}] + mf.write_text("\n".join(json.dumps(r) for r in records) + "\n") + loaded = load_manifest(mf) + assert len(loaded) == 2 + assert loaded[0]["uuid"] == "a" + assert loaded[1]["uuid"] == "b" + + def test_skips_blank_lines(self, tmp_path): + mf = tmp_path / "manifest.jsonl" + mf.write_text('{"uuid":"a"}\n\n{"uuid":"b"}\n') + assert len(load_manifest(mf)) == 2 + + def test_skips_malformed_json_lines(self, tmp_path): + mf = tmp_path / "manifest.jsonl" + mf.write_text('{"uuid":"a"}\nNOT_JSON\n{"uuid":"b"}\n') + loaded = load_manifest(mf) + assert len(loaded) == 2 + + def test_empty_file_returns_empty_list(self, tmp_path): + mf = tmp_path / "manifest.jsonl" + mf.write_text("") + assert load_manifest(mf) == [] + + +# --------------------------------------------------------------------------- +# validate_entry +# --------------------------------------------------------------------------- + +class TestValidateEntryValid: + def test_returns_dict_for_valid_record(self, tmp_path): + rec = _record(tmp_path) + entry = validate_entry(rec, min_duration=1.0, min_chars=3) + assert entry is not None + assert entry["uuid"] == "abc123" + assert entry["raw_transcription"] == "hello world" + + def test_corrected_transcription_preserved(self, tmp_path): + rec = _record(tmp_path, corrected="Hello World.") + entry = validate_entry(rec, min_duration=1.0, min_chars=3) + assert entry["corrected_transcription"] == "Hello World." + + def test_corrected_falls_back_to_raw_when_empty(self, tmp_path): + rec = _record(tmp_path, corrected="") + entry = validate_entry(rec, min_duration=1.0, min_chars=3) + assert entry["corrected_transcription"] == "hello world" + + def test_is_improved_parsed_as_bool(self, tmp_path): + rec = _record(tmp_path) + rec["is_improved"] = "True" + entry = validate_entry(rec, min_duration=1.0, min_chars=3) + assert entry["is_improved"] is True + + def test_is_improved_false_string(self, tmp_path): + rec = _record(tmp_path) + rec["is_improved"] = "False" + entry = validate_entry(rec, min_duration=1.0, min_chars=3) + assert entry["is_improved"] is False + + def test_audio_path_resolved_to_absolute(self, tmp_path): + rec = _record(tmp_path) + entry = validate_entry(rec, min_duration=1.0, min_chars=3) + assert Path(entry["audio_path"]).is_absolute() + + +class TestValidateEntryRejects: + def test_missing_uuid_returns_none(self, tmp_path): + rec = _record(tmp_path) + del rec["uuid"] + assert validate_entry(rec, min_duration=0.0, min_chars=0) is None + + def test_empty_uuid_returns_none(self, tmp_path): + rec = _record(tmp_path) + rec["uuid"] = "" + assert validate_entry(rec, min_duration=0.0, min_chars=0) is None + + def test_missing_raw_transcription_returns_none(self, tmp_path): + rec = _record(tmp_path) + del rec["raw_transcription"] + assert validate_entry(rec, min_duration=0.0, min_chars=0) is None + + def test_missing_audio_path_field_returns_none(self, tmp_path): + rec = _record(tmp_path) + del rec["audio_path"] + assert validate_entry(rec, min_duration=0.0, min_chars=0) is None + + def test_duration_below_minimum_returns_none(self, tmp_path): + rec = _record(tmp_path, duration_s=0.3) + assert validate_entry(rec, min_duration=0.5, min_chars=0) is None + + def test_duration_exactly_at_minimum_passes(self, tmp_path): + rec = _record(tmp_path, duration_s=0.5) + assert validate_entry(rec, min_duration=0.5, min_chars=0) is not None + + def test_transcript_too_short_returns_none(self, tmp_path): + rec = _record(tmp_path, raw="hi") # 2 non-whitespace chars + assert validate_entry(rec, min_duration=0.0, min_chars=5) is None + + def test_missing_audio_file_returns_none(self, tmp_path): + rec = _record(tmp_path, create_file=False) + assert validate_entry(rec, min_duration=0.0, min_chars=0) is None + + +class TestValidateEntryTagStripping: + def test_noise_tags_stripped_by_default(self, tmp_path): + rec = _record(tmp_path, raw="(music) hello world") + entry = validate_entry(rec, min_duration=0.0, min_chars=3, strip_tags=True) + assert entry is not None + assert "(music)" not in entry["raw_transcription"] + + def test_noise_tags_preserved_when_strip_false(self, tmp_path): + rec = _record(tmp_path, raw="(music) hello world") + entry = validate_entry(rec, min_duration=0.0, min_chars=3, strip_tags=False) + assert entry is not None + assert "(music)" in entry["raw_transcription"] + + def test_pure_noise_tag_rejected_after_strip(self, tmp_path): + # "(music)" alone → after stripping → empty → too short → None + rec = _record(tmp_path, raw="(music)") + assert validate_entry(rec, min_duration=0.0, min_chars=3, strip_tags=True) is None + + def test_pure_noise_tag_accepted_without_strip(self, tmp_path): + # With strip_tags=False the 7 chars of "(music)" pass min_chars=3 + rec = _record(tmp_path, raw="(music)") + assert validate_entry(rec, min_duration=0.0, min_chars=3, strip_tags=False) is not None + + +# --------------------------------------------------------------------------- +# parse_split +# --------------------------------------------------------------------------- + +class TestParseSplit: + def test_equal_thirds(self): + train, dev, test = parse_split("80/10/10") + assert abs(train - 0.8) < 1e-9 + assert abs(dev - 0.1) < 1e-9 + assert abs(test - 0.1) < 1e-9 + + def test_fractions_sum_to_one(self): + fracs = parse_split("70/15/15") + assert abs(sum(fracs) - 1.0) < 1e-9 + + def test_non_percentage_ratio_normalised(self): + train, dev, test = parse_split("1/1/1") + assert abs(train - 1 / 3) < 1e-9 + + def test_wrong_number_of_parts_raises(self): + with pytest.raises(ValueError): + parse_split("80/20") + + def test_zero_total_raises(self): + with pytest.raises(ValueError): + parse_split("0/0/0") + + def test_non_numeric_raises(self): + with pytest.raises(ValueError): + parse_split("a/b/c") + + +# --------------------------------------------------------------------------- +# assign_splits +# --------------------------------------------------------------------------- + +class TestAssignSplits: + def _make_entries(self, n: int) -> list[dict]: + return [{"uuid": str(i)} for i in range(n)] + + def test_all_entries_assigned_a_split(self): + entries = assign_splits(self._make_entries(10), 0.8, 0.1) + assert all("split" in e for e in entries) + assert all(e["split"] in ("train", "dev", "test") for e in entries) + + def test_split_counts_approximate_fractions(self): + entries = assign_splits(self._make_entries(100), 0.8, 0.1, seed=0) + counts = {"train": 0, "dev": 0, "test": 0} + for e in entries: + counts[e["split"]] += 1 + assert counts["train"] == 80 + assert counts["dev"] == 10 + assert counts["test"] == 10 + + def test_deterministic_with_same_seed(self): + a = assign_splits(self._make_entries(20), 0.8, 0.1, seed=42) + b = assign_splits(self._make_entries(20), 0.8, 0.1, seed=42) + assert [e["split"] for e in a] == [e["split"] for e in b] + + def test_different_seeds_differ(self): + a = assign_splits(self._make_entries(20), 0.8, 0.1, seed=1) + b = assign_splits(self._make_entries(20), 0.8, 0.1, seed=2) + # The label sequence is always 16×train, 2×dev, 2×test — but which + # UUID ends up in which bucket differs by seed; compare those sets. + def _train_uuids(result): + return {e["uuid"] for e in result if e["split"] == "train"} + assert _train_uuids(a) != _train_uuids(b) + + def test_does_not_mutate_original_list(self): + orig = self._make_entries(10) + ids_before = [e["uuid"] for e in orig] + assign_splits(orig, 0.8, 0.1) + assert [e["uuid"] for e in orig] == ids_before + + def test_single_entry_assigned_test(self): + entries = assign_splits([{"uuid": "x"}], 0.8, 0.1) + assert entries[0]["split"] == "test" + + +# --------------------------------------------------------------------------- +# write_csv +# --------------------------------------------------------------------------- + +class TestWriteCsv: + def _sample_entries(self, tmp_path: Path, n: int = 3) -> list[dict]: + entries = [] + for i in range(n): + wav = tmp_path / f"clip_{i}.wav" + _wav(wav) + entries.append({ + "uuid": f"u{i}", + "split": "train" if i < 2 else "test", + "audio_path": str(wav), + "raw_transcription": f"text {i}", + "corrected_transcription": f"text {i}", + "is_improved": False, + "duration_s": 2.0, + "sample_rate": 16000, + "whisper_model": "base", + "llm_model": "", + "timestamp": "2025-01-01T00:00:00", + }) + return entries + + def test_creates_csv_file(self, tmp_path): + entries = self._sample_entries(tmp_path) + out = write_csv(entries, tmp_path, "train") + assert out.exists() + assert out.name == "train.csv" + + def test_csv_has_correct_columns(self, tmp_path): + entries = self._sample_entries(tmp_path) + out = write_csv(entries, tmp_path, "train") + with open(out, newline="", encoding="utf-8") as f: + reader = csv.DictReader(f) + assert list(reader.fieldnames) == CSV_COLUMNS + + def test_csv_contains_only_requested_split(self, tmp_path): + entries = self._sample_entries(tmp_path) # 2 train, 1 test + out = write_csv(entries, tmp_path, "train") + with open(out, newline="", encoding="utf-8") as f: + rows = list(csv.DictReader(f)) + assert len(rows) == 2 + assert all(r["split"] == "train" for r in rows) + + def test_empty_split_writes_header_only(self, tmp_path): + entries = self._sample_entries(tmp_path) + # No "dev" entries in the sample + out = write_csv(entries, tmp_path, "dev") + with open(out, newline="", encoding="utf-8") as f: + rows = list(csv.DictReader(f)) + assert rows == [] + + def test_csv_row_values_match_entries(self, tmp_path): + entries = self._sample_entries(tmp_path) + out = write_csv(entries, tmp_path, "train") + with open(out, newline="", encoding="utf-8") as f: + rows = list(csv.DictReader(f)) + uuids = {r["uuid"] for r in rows} + assert uuids == {"u0", "u1"} + + +# --------------------------------------------------------------------------- +# remap_audio_prefix logic (extracted from main()) +# --------------------------------------------------------------------------- + +class TestRemapAudioPrefix: + """Test the path-rewrite logic used by --remap-audio-prefix.""" + + def _remap(self, records: list[dict], old: str, new: str) -> list[dict]: + """Apply the same remap loop as main().""" + import copy + records = copy.deepcopy(records) + for rec in records: + p = rec.get("audio_path", "") + if p.startswith(old): + rec["audio_path"] = new + p[len(old):] + return records + + def test_prefix_replaced(self): + records = [{"audio_path": "/host/audio/clip.wav"}] + result = self._remap(records, "/host/audio", "/container/audio") + assert result[0]["audio_path"] == "/container/audio/clip.wav" + + def test_only_matching_prefix_replaced(self): + records = [ + {"audio_path": "/host/audio/a.wav"}, + {"audio_path": "/other/path/b.wav"}, + ] + result = self._remap(records, "/host/audio", "/container/audio") + assert result[0]["audio_path"] == "/container/audio/a.wav" + assert result[1]["audio_path"] == "/other/path/b.wav" + + def test_nested_path_preserved_after_prefix(self): + records = [{"audio_path": "/host/audio/2025/01/clip.wav"}] + result = self._remap(records, "/host/audio", "/data") + assert result[0]["audio_path"] == "/data/2025/01/clip.wav" + + def test_no_trailing_slash_double_slash(self): + """Ensure no double-slash when old path ends with slash.""" + records = [{"audio_path": "/host/audio/clip.wav"}] + result = self._remap(records, "/host/audio/", "/container/audio/") + # "/host/audio/" prefix + "clip.wav" suffix → "/container/audio/clip.wav" + assert result[0]["audio_path"] == "/container/audio/clip.wav" + + def test_empty_audio_path_unaffected(self): + records = [{"audio_path": ""}] + result = self._remap(records, "/host", "/container") + assert result[0]["audio_path"] == "" + + def test_original_records_not_mutated(self): + records = [{"audio_path": "/host/clip.wav"}] + self._remap(records, "/host", "/container") + # Original should be unchanged (we deepcopy in helper) + assert records[0]["audio_path"] == "/host/clip.wav" From 80bc8e21a01d3f4668c85f26605da20500481876 Mon Sep 17 00:00:00 2001 From: rebreda Date: Sat, 7 Mar 2026 08:41:08 -0500 Subject: [PATCH 09/11] Skip ci finetune tests since it requires torch --- tests/test_finetune_model.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_finetune_model.py b/tests/test_finetune_model.py index c38e5c0..f7e130e 100644 --- a/tests/test_finetune_model.py +++ b/tests/test_finetune_model.py @@ -10,7 +10,8 @@ from unittest.mock import MagicMock, patch import pytest -import torch + +torch = pytest.importorskip("torch") import torch.nn as nn From 825efbbd07184f4effb1282e2da4f57d6ae39829 Mon Sep 17 00:00:00 2001 From: rebreda Date: Sat, 7 Mar 2026 08:45:08 -0500 Subject: [PATCH 10/11] Skip ci finetune tests since it requires torch --- tests/test_finetune_data.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_finetune_data.py b/tests/test_finetune_data.py index 1cedf08..5df2677 100644 --- a/tests/test_finetune_data.py +++ b/tests/test_finetune_data.py @@ -124,6 +124,7 @@ class TestWhisperDataCollator: @pytest.fixture() def collator(self): + pytest.importorskip("torch") processor = _make_processor(pad_token_id=50256) return WhisperDataCollator(processor=processor, decoder_start_token_id=50258) From da3522ddd02d2cc2fc63d2c09eb7df9e5c24ea0e Mon Sep 17 00:00:00 2001 From: rebreda Date: Sat, 7 Mar 2026 08:49:27 -0500 Subject: [PATCH 11/11] doc edit --- src/listenr/build_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/listenr/build_dataset.py b/src/listenr/build_dataset.py index b8f055b..3f28187 100644 --- a/src/listenr/build_dataset.py +++ b/src/listenr/build_dataset.py @@ -318,7 +318,7 @@ def main() -> None: "Rewrite the leading path component of every audio_path in the manifest. " "Useful when running inside a container where the host path is mounted at " "a different location. Example: " - "--remap-audio-prefix /home/g/.listenr/audio_clips:/data/listenr/audio_clips" + "--remap-audio-prefix /home/you/.listenr/audio_clips:/data/listenr/audio_clips" ), ) parser.add_argument(