From ecba91b332610d32e7f20b0cfa92e746a2f024e3 Mon Sep 17 00:00:00 2001 From: xdevfaheem Date: Fri, 21 Feb 2025 13:12:20 +0530 Subject: [PATCH 01/10] added 'spacy-layout' lib to dependencies --- pyproject.toml | 1 + uv.lock | 1479 +++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 1466 insertions(+), 14 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 130167e..e55698f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,6 +19,7 @@ dependencies = [ "soundfile>=0.13.1", "huggingface-hub>=0.28.1", "gradio>=5.15.0", + "spacy-layout>=0.0.11", ] # These are technically optional, but mamba-ssm is required to run hybrid models. diff --git a/uv.lock b/uv.lock index de16900..2b78ca1 100644 --- a/uv.lock +++ b/uv.lock @@ -1,10 +1,22 @@ version = 1 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version == '3.12.*'", - "python_full_version == '3.11.*'", - "python_full_version < '3.11'", + "python_full_version >= 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Subject: [PATCH 02/10] moved gradio_interface.py to its specific dir --- .../main.py | 20 ++++++++++++++----- gradio_interface/utils.py | 1 + 2 files changed, 16 insertions(+), 5 deletions(-) rename gradio_interface.py => gradio_interface/main.py (95%) create mode 100644 gradio_interface/utils.py diff --git a/gradio_interface.py b/gradio_interface/main.py similarity index 95% rename from gradio_interface.py rename to gradio_interface/main.py index c112259..0c22d83 100644 --- a/gradio_interface.py +++ b/gradio_interface/main.py @@ -2,7 +2,6 @@ import torchaudio import gradio as gr from os import getenv - from zonos.model import Zonos, DEFAULT_BACKBONE_CLS as ZonosBackbone from zonos.conditioning import make_cond_dict, supported_language_codes from zonos.utils import DEFAULT_DEVICE as device @@ -268,10 +267,21 @@ def build_interface(): with gr.Row(): with gr.Column(): gr.Markdown("### NovelAi's unified sampler") - linear_slider = gr.Slider(-2.0, 2.0, 0.5, 0.01, label="Linear (set to 0 to disable unified sampling)", info="High values make the output less random.") - #Conf's theoretical range is between -2 * Quad and 0. - confidence_slider = gr.Slider(-2.0, 2.0, 0.40, 0.01, label="Confidence", info="Low values make random outputs more random.") - quadratic_slider = gr.Slider(-2.0, 2.0, 0.00, 0.01, label="Quadratic", info="High values make low probablities much lower.") + linear_slider = gr.Slider( + -2.0, + 2.0, + 0.5, + 0.01, + label="Linear (set to 0 to disable unified sampling)", + info="High values make the output less random.", + ) + # Conf's theoretical range is between -2 * Quad and 0. + confidence_slider = gr.Slider( + -2.0, 2.0, 0.40, 0.01, label="Confidence", info="Low values make random outputs more random." + ) + quadratic_slider = gr.Slider( + -2.0, 2.0, 0.00, 0.01, label="Quadratic", info="High values make low probablities much lower." + ) with gr.Column(): gr.Markdown("### Legacy sampling") top_p_slider = gr.Slider(0.0, 1.0, 0, 0.01, label="Top P") diff --git a/gradio_interface/utils.py b/gradio_interface/utils.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/gradio_interface/utils.py @@ -0,0 +1 @@ + From ed3503ab21a0a8cb8aac1ff72fa9aa4ec7b0baaf Mon Sep 17 00:00:00 2001 From: xdevfaheem Date: Fri, 21 Feb 2025 18:11:15 +0530 Subject: [PATCH 03/10] Added file input and streaming support --- README.md | 8 ++ gradio_interface/main.py | 244 +++++++++++++++++++++++--------------- gradio_interface/utils.py | 44 +++++++ 3 files changed, 198 insertions(+), 98 deletions(-) diff --git a/README.md b/README.md index d510c75..939289d 100644 --- a/README.md +++ b/README.md @@ -78,10 +78,13 @@ _For repeated sampling we highly recommend using the gradio interface instead, a ## Features - Zero-shot TTS with voice cloning: Input desired text and a 10-30s speaker sample to generate high quality TTS output +- Text/File inputs: Support both written text and document (TXT, PDF, XLSX, DOCX and other [Docling](https://ds4sd.github.io/docling/supported_formats/) supported formats) +- Unlimited content: Support infinite content length due to the chunked generation + streaming - Audio prefix inputs: Add text plus an audio prefix for even richer speaker matching. Audio prefixes can be used to elicit behaviours such as whispering which can otherwise be challenging to replicate when cloning from speaker embeddings - Multilingual support: Zonos-v0.1 supports English, Japanese, Chinese, French, and German - Audio quality and emotion control: Zonos offers fine-grained control of many aspects of the generated audio. These include speaking rate, pitch, maximum frequency, audio quality, and various emotions such as happiness, anger, sadness, and fear. - Fast: our model runs with a real-time factor of ~2x on an RTX 4090 (i.e. generates 2 seconds of audio per 1 second of compute time) +- Streaming: Get the audio (of each text chunk) as soon as it is generated - Gradio WebUI: Zonos comes packaged with an easy to use gradio interface to generate speech - Simple installation and deployment: Zonos can be installed and deployed simply using the docker file packaged with our repository. @@ -133,6 +136,11 @@ pip install -e . pip install --no-build-isolation -e .[compile] # optional but needed to run the hybrid ``` +Finally, Install spacy's language model (for text processing) into the activated system/environment +```bash +python -m spacy download en_core_web_trf +``` + ##### Confirm that it's working For convenience we provide a minimal example to check that the installation works: diff --git a/gradio_interface/main.py b/gradio_interface/main.py index 0c22d83..a59699c 100644 --- a/gradio_interface/main.py +++ b/gradio_interface/main.py @@ -1,7 +1,10 @@ +import tempfile +from scipy.stats import yeojohnson import torch import torchaudio import gradio as gr from os import getenv +from .utils import process_file from zonos.model import Zonos, DEFAULT_BACKBONE_CLS as ZonosBackbone from zonos.conditioning import make_cond_dict, supported_language_codes from zonos.utils import DEFAULT_DEVICE as device @@ -37,6 +40,7 @@ def update_ui(model_choice): print("Conditioners in this model:", cond_names) text_update = gr.update(visible=("espeak" in cond_names)) + file_update = gr.update(visible=("espeak" in cond_names)) language_update = gr.update(visible=("espeak" in cond_names)) speaker_audio_update = gr.update(visible=("speaker" in cond_names)) prefix_audio_update = gr.update(visible=True) @@ -60,6 +64,7 @@ def update_ui(model_choice): return ( text_update, + file_update, language_update, speaker_audio_update, prefix_audio_update, @@ -84,6 +89,7 @@ def update_ui(model_choice): def generate_audio( model_choice, text, + file, language, speaker_audio, prefix_audio, @@ -140,66 +146,101 @@ def generate_audio( if randomize_seed: seed = torch.randint(0, 2**32 - 1, (1,)).item() torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + try: + # Handle input content + content_path = None + if file is not None: + content_path = file + elif text.strip(): + # Save text to temporary file + with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f: + f.write(text) + content_path = f.name + + if not content_path: + raise gr.Error("Please provide either text input or upload a document file") + + if speaker_audio is not None and "speaker" not in unconditional_keys: + if speaker_audio != SPEAKER_AUDIO_PATH: + print("Recomputed speaker embedding") + wav, sr = torchaudio.load(speaker_audio) + SPEAKER_EMBEDDING = selected_model.make_speaker_embedding(wav, sr) + SPEAKER_EMBEDDING = SPEAKER_EMBEDDING.to(device, dtype=torch.bfloat16) + SPEAKER_AUDIO_PATH = speaker_audio + + audio_prefix_codes = None + if prefix_audio is not None: + wav_prefix, sr_prefix = torchaudio.load(prefix_audio) + wav_prefix = wav_prefix.mean(0, keepdim=True) + wav_prefix = selected_model.autoencoder.preprocess(wav_prefix, sr_prefix) + wav_prefix = wav_prefix.to(device, dtype=torch.float32) + audio_prefix_codes = selected_model.autoencoder.encode(wav_prefix.unsqueeze(0)) + + emotion_tensor = torch.tensor(list(map(float, [e1, e2, e3, e4, e5, e6, e7, e8])), device=device) + + vq_val = float(vq_single) + vq_tensor = torch.tensor([vq_val] * 8, device=device).unsqueeze(0) + + text_chunks = process_file(content_path, 35) # hard coded for now, could add a slider + total_chunks = len(text_chunks) + gr.Info(f"Text split into {total_chunks} chunks. Starting synthesis...") + + # Chunked Generation (unlimited content, yay!) + for idx, chunk in enumerate(text_chunks, 1): + if chunk.strip(): + continue + gr.Info(f"Generating chunk {idx}/{total_chunks}...") + + estimated_generation_duration = 30 * len(chunk) / 400 + estimated_total_steps = int(estimated_generation_duration * 86) + + def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool: + progress((step, estimated_total_steps)) + return True + + cond_dict = make_cond_dict( + text=chunk.strip(), + language=language, + speaker=SPEAKER_EMBEDDING, + emotion=emotion_tensor, + vqscore_8=vq_tensor, + fmax=fmax, + pitch_std=pitch_std, + speaking_rate=speaking_rate, + dnsmos_ovrl=dnsmos_ovrl, + speaker_noised=speaker_noised_bool, + device=device, + unconditional_keys=unconditional_keys, + ) + conditioning = selected_model.prepare_conditioning(cond_dict) + + codes = selected_model.generate( + prefix_conditioning=conditioning, + audio_prefix_codes=audio_prefix_codes, + max_new_tokens=max_new_tokens, + cfg_scale=cfg_scale, + batch_size=1, + sampling_params=dict( + top_p=top_p, top_k=top_k, min_p=min_p, linear=linear, conf=confidence, quad=quadratic + ), + callback=update_progress, + ) - if speaker_audio is not None and "speaker" not in unconditional_keys: - if speaker_audio != SPEAKER_AUDIO_PATH: - print("Recomputed speaker embedding") - wav, sr = torchaudio.load(speaker_audio) - SPEAKER_EMBEDDING = selected_model.make_speaker_embedding(wav, sr) - SPEAKER_EMBEDDING = SPEAKER_EMBEDDING.to(device, dtype=torch.bfloat16) - SPEAKER_AUDIO_PATH = speaker_audio - - audio_prefix_codes = None - if prefix_audio is not None: - wav_prefix, sr_prefix = torchaudio.load(prefix_audio) - wav_prefix = wav_prefix.mean(0, keepdim=True) - wav_prefix = selected_model.autoencoder.preprocess(wav_prefix, sr_prefix) - wav_prefix = wav_prefix.to(device, dtype=torch.float32) - audio_prefix_codes = selected_model.autoencoder.encode(wav_prefix.unsqueeze(0)) - - emotion_tensor = torch.tensor(list(map(float, [e1, e2, e3, e4, e5, e6, e7, e8])), device=device) - - vq_val = float(vq_single) - vq_tensor = torch.tensor([vq_val] * 8, device=device).unsqueeze(0) - - cond_dict = make_cond_dict( - text=text, - language=language, - speaker=SPEAKER_EMBEDDING, - emotion=emotion_tensor, - vqscore_8=vq_tensor, - fmax=fmax, - pitch_std=pitch_std, - speaking_rate=speaking_rate, - dnsmos_ovrl=dnsmos_ovrl, - speaker_noised=speaker_noised_bool, - device=device, - unconditional_keys=unconditional_keys, - ) - conditioning = selected_model.prepare_conditioning(cond_dict) - - estimated_generation_duration = 30 * len(text) / 400 - estimated_total_steps = int(estimated_generation_duration * 86) - - def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool: - progress((step, estimated_total_steps)) - return True - - codes = selected_model.generate( - prefix_conditioning=conditioning, - audio_prefix_codes=audio_prefix_codes, - max_new_tokens=max_new_tokens, - cfg_scale=cfg_scale, - batch_size=1, - sampling_params=dict(top_p=top_p, top_k=top_k, min_p=min_p, linear=linear, conf=confidence, quad=quadratic), - callback=update_progress, - ) + wav_out = selected_model.autoencoder.decode(codes).cpu().detach() + sr_out = selected_model.autoencoder.sampling_rate + if wav_out.dim() == 2 and wav_out.size(0) > 1: + wav_out = wav_out[0:1, :] - wav_out = selected_model.autoencoder.decode(codes).cpu().detach() - sr_out = selected_model.autoencoder.sampling_rate - if wav_out.dim() == 2 and wav_out.size(0) > 1: - wav_out = wav_out[0:1, :] - return (sr_out, wav_out.squeeze().numpy()), seed + # add sub-sec silence between chunks + silence = torch.zeros(1, int(selected_model.autoencoder.sampling_rate * 0.4)) + wav_out = torch.cat([wav_out, silence], dim=1) + yield (sr_out, wav_out.squeeze().numpy()), seed + + except Exception as e: + gr.Error(str(e)) def build_interface(): @@ -218,18 +259,22 @@ def build_interface(): with gr.Blocks() as demo: with gr.Row(): with gr.Column(): + with gr.Tab("Text Input"): + text = gr.Textbox( + label="Text to Synthesize", + placeholder="Enter the text you want to convert to speech...", + lines=5, + max_length=500, + ) + with gr.Tab("File Upload"): + file = gr.File(label="Upload Document", file_types=[".txt", ".pdf", ".xlsx", ".docx"]) model_choice = gr.Dropdown( choices=supported_models, value=supported_models[0], label="Zonos Model Type", info="Select the model variant to use.", ) - text = gr.Textbox( - label="Text to Synthesize", - value="Zonos uses eSpeak for text to phoneme conversion!", - lines=4, - max_length=500, # approximately - ) + language = gr.Dropdown( choices=supported_language_codes, value="en-us", @@ -243,7 +288,7 @@ def build_interface(): ) with gr.Column(): speaker_audio = gr.Audio( - label="Optional Speaker Audio (for cloning)", + label="Optional Speaker Audio Sample (for cloning)", type="filepath", ) speaker_noised_checkbox = gr.Checkbox(label="Denoise Speaker?", value=False) @@ -316,36 +361,37 @@ def build_interface(): "Certain configurations can cause the model to become unstable. Setting emotion to unconditional may help." ) with gr.Row(): - emotion1 = gr.Slider(0.0, 1.0, 1.0, 0.05, label="Happiness") - emotion2 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Sadness") - emotion3 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Disgust") - emotion4 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Fear") + happiness = gr.Slider(0.0, 1.0, 1.0, 0.05, label="Happiness") + sadness = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Sadness") + disgust = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Disgust") + fear = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Fear") with gr.Row(): - emotion5 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Surprise") - emotion6 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Anger") - emotion7 = gr.Slider(0.0, 1.0, 0.1, 0.05, label="Other") - emotion8 = gr.Slider(0.0, 1.0, 0.2, 0.05, label="Neutral") + surprise = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Surprise") + anger = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Anger") + other = gr.Slider(0.0, 1.0, 0.1, 0.05, label="Other") + neutral = gr.Slider(0.0, 1.0, 0.2, 0.05, label="Neutral") with gr.Column(): generate_button = gr.Button("Generate Audio") - output_audio = gr.Audio(label="Generated Audio", type="numpy", autoplay=True) + output_audio = gr.Audio(label="Generated Audio", type="numpy", streaming=True, autoplay=True) model_choice.change( fn=update_ui, inputs=[model_choice], outputs=[ text, + file, language, speaker_audio, prefix_audio, - emotion1, - emotion2, - emotion3, - emotion4, - emotion5, - emotion6, - emotion7, - emotion8, + happiness, + sadness, + disgust, + fear, + surprise, + anger, + other, + neutral, vq_single_slider, fmax_slider, pitch_std_slider, @@ -362,17 +408,18 @@ def build_interface(): inputs=[model_choice], outputs=[ text, + file, language, speaker_audio, prefix_audio, - emotion1, - emotion2, - emotion3, - emotion4, - emotion5, - emotion6, - emotion7, - emotion8, + happiness, + sadness, + disgust, + fear, + surprise, + anger, + other, + neutral, vq_single_slider, fmax_slider, pitch_std_slider, @@ -389,17 +436,18 @@ def build_interface(): inputs=[ model_choice, text, + file, language, speaker_audio, prefix_audio, - emotion1, - emotion2, - emotion3, - emotion4, - emotion5, - emotion6, - emotion7, - emotion8, + happiness, + sadness, + disgust, + fear, + surprise, + anger, + other, + neutral, vq_single_slider, fmax_slider, pitch_std_slider, diff --git a/gradio_interface/utils.py b/gradio_interface/utils.py index 8b13789..790db5e 100644 --- a/gradio_interface/utils.py +++ b/gradio_interface/utils.py @@ -1 +1,45 @@ +import os +import spacy +from spacy_layout import spaCyLayout + +def process_file(file: str, words_per_chunk_seconds=32): + nlp = spacy.load("en_core_web_trf") + + # Extract content + if file.endswith(".txt") and os.path.isfile(file): + with open(file, "r") as f: + content = f.read() + elif (file.endswith(".pdf") or file.endswith(".xlsx") or file.endswith("docx")) and os.path.isfile(file): + parser = spaCyLayout(nlp) + doc = parser(file) + content = doc.text + elif not os.path.isfile(file): + content = file + else: + raise Exception( + "Unsupported file type, Come on there! why not, have your content in txt or pdf? it's simple and convinient isn't it?" + ) + + num_words = 0 + current_split = 0 + chunks = [] + + # split te extracted content + # later: https://github.com/segment-any-text/wtpsplit + doc = nlp(content) + splits = [sent.text.strip() for sent in doc.sents] + + # generate chunks with words less the word count limit + for i in range(len(splits)): + sent = splits[i] + sentence_per_words = len(sent.split()) + if num_words + sentence_per_words >= words_per_chunk_seconds: + text = " ".join(splits[current_split:i]) + chunks.append(text) + num_words = 0 + current_split = i + num_words += sentence_per_words + + chunks.append(" ".join(splits[current_split:])) # flush the remaining splits + return chunks From b2145fee1deb12ae0a56e4d50e48beab20fb57d2 Mon Sep 17 00:00:00 2001 From: xdevfaheem Date: Fri, 21 Feb 2025 19:14:31 +0530 Subject: [PATCH 04/10] added spacy language model to dependency --- README.md | 5 ---- pyproject.toml | 4 +++ uv.lock | 68 ++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 72 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 939289d..55a29b4 100644 --- a/README.md +++ b/README.md @@ -136,11 +136,6 @@ pip install -e . pip install --no-build-isolation -e .[compile] # optional but needed to run the hybrid ``` -Finally, Install spacy's language model (for text processing) into the activated system/environment -```bash -python -m spacy download en_core_web_trf -``` - ##### Confirm that it's working For convenience we provide a minimal example to check that the installation works: diff --git a/pyproject.toml b/pyproject.toml index e55698f..b044e6e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ dependencies = [ "huggingface-hub>=0.28.1", "gradio>=5.15.0", "spacy-layout>=0.0.11", + "en-core-web-trf", ] # These are technically optional, but mamba-ssm is required to run hybrid models. @@ -36,5 +37,8 @@ include = ["zonos"] [tool.uv] no-build-isolation-package = ["flash-attn", "mamba-ssm", "causal-conv1d"] +[tool.uv.sources] +en-core-web-trf = { url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.8.0/en_core_web_trf-3.8.0-py3-none-any.whl" } + [tool.ruff] line-length = 120 diff --git a/uv.lock b/uv.lock index 2b78ca1..21de2b5 100644 --- a/uv.lock +++ b/uv.lock @@ -401,6 +401,44 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/13/84/069db1325f5a6dd034524c0099da3978de8fdb6242ce63223ead188da940/csvw-3.5.1-py2.py3-none-any.whl", hash = "sha256:8dd3864aae51bfd943713a62ec2c6688d3f406a9627b5f16de2479b1281febe5", size = 59881 }, ] +[[package]] +name = "curated-tokenizers" +version = "0.0.9" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "regex" }, +] +sdist = { url = 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"sha256:ddfd33e81b53ad798dac841ab022189f9543718ff874eda1081fce6ff93de377", size = 236322 }, +] + [[package]] name = "spacy-layout" version = "0.0.11" @@ -3555,6 +3621,7 @@ name = "zonos" version = "0.1.0" source = { virtual = "." } dependencies = [ + { name = "en-core-web-trf" }, { name = "gradio" }, { name = "huggingface-hub" }, { name = "inflect" }, @@ -3582,6 +3649,7 @@ compile = [ [package.metadata] requires-dist = [ { name = "causal-conv1d", marker = "extra == 'compile'", specifier = ">=1.5.0.post8" }, + { name = "en-core-web-trf", url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.8.0/en_core_web_trf-3.8.0-py3-none-any.whl" }, { name = "flash-attn", marker = "extra == 'compile'", specifier = ">=2.7.3" }, { name = "gradio", specifier = ">=5.15.0" }, { name = "huggingface-hub", specifier = ">=0.28.1" }, From 1a046756b9ce45389d1c5a081dad481d178de68e Mon Sep 17 00:00:00 2001 From: Mohammed Faheem Date: Sat, 22 Feb 2025 01:40:26 +0530 Subject: [PATCH 05/10] fix import --- gradio_interface/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gradio_interface/main.py b/gradio_interface/main.py index a59699c..dd32a37 100644 --- a/gradio_interface/main.py +++ b/gradio_interface/main.py @@ -4,7 +4,7 @@ import torchaudio import gradio as gr from os import getenv -from .utils import process_file +from utils import process_file from zonos.model import Zonos, DEFAULT_BACKBONE_CLS as ZonosBackbone from zonos.conditioning import make_cond_dict, supported_language_codes from zonos.utils import DEFAULT_DEVICE as device From 3bcfae51ab6a297a41b19f87b7c6e23f6072efc6 Mon Sep 17 00:00:00 2001 From: xdevfaheem Date: Sat, 22 Feb 2025 12:46:08 +0530 Subject: [PATCH 06/10] bug fixes + tweaks --- gradio_interface/main.py | 20 +++++++++++++------- gradio_interface/utils.py | 9 +++++++++ 2 files changed, 22 insertions(+), 7 deletions(-) diff --git a/gradio_interface/main.py b/gradio_interface/main.py index dd32a37..c961cb9 100644 --- a/gradio_interface/main.py +++ b/gradio_interface/main.py @@ -1,10 +1,9 @@ import tempfile -from scipy.stats import yeojohnson import torch import torchaudio import gradio as gr from os import getenv -from utils import process_file +from utils import process_file, float32_to_int16 from zonos.model import Zonos, DEFAULT_BACKBONE_CLS as ZonosBackbone from zonos.conditioning import make_cond_dict, supported_language_codes from zonos.utils import DEFAULT_DEVICE as device @@ -190,7 +189,7 @@ def generate_audio( # Chunked Generation (unlimited content, yay!) for idx, chunk in enumerate(text_chunks, 1): - if chunk.strip(): + if not chunk.strip(): continue gr.Info(f"Generating chunk {idx}/{total_chunks}...") @@ -231,13 +230,20 @@ def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool: wav_out = selected_model.autoencoder.decode(codes).cpu().detach() sr_out = selected_model.autoencoder.sampling_rate + if wav_out.dim() == 3: + wav_out = wav_out.squeeze(0) + if wav_out.dim() == 1: + wav_out = wav_out.unsqueeze(0) if wav_out.dim() == 2 and wav_out.size(0) > 1: wav_out = wav_out[0:1, :] - # add sub-sec silence between chunks - silence = torch.zeros(1, int(selected_model.autoencoder.sampling_rate * 0.4)) - wav_out = torch.cat([wav_out, silence], dim=1) - yield (sr_out, wav_out.squeeze().numpy()), seed + if idx != total_chunks: + # add sub-sec silence between chunks + silence = torch.zeros(1, int(sr_out * 0.3)) + wav_out = torch.cat([wav_out, silence], dim=1) + # gradio audio streaming except 16bit int array + wav_array = float32_to_int16(wav_out.squeeze().numpy()) + yield (sr_out, wav_array), seed except Exception as e: gr.Error(str(e)) diff --git a/gradio_interface/utils.py b/gradio_interface/utils.py index 790db5e..216f517 100644 --- a/gradio_interface/utils.py +++ b/gradio_interface/utils.py @@ -1,6 +1,7 @@ import os import spacy from spacy_layout import spaCyLayout +import numpy as np def process_file(file: str, words_per_chunk_seconds=32): @@ -43,3 +44,11 @@ def process_file(file: str, words_per_chunk_seconds=32): chunks.append(" ".join(splits[current_split:])) # flush the remaining splits return chunks + + +def float32_to_int16(audio_array): + if np.issubdtype(audio_array.dtype, np.floating): + max_val = np.max(np.abs(audio_array)) + # Scale and convert + int16_array = np.clip((audio_array / max_val) * 32767, -32768, 32767).astype(np.int16) + return int16_array From dd7edbf48a4026facea9baedc1b09070844bfaa7 Mon Sep 17 00:00:00 2001 From: xdevfaheem Date: Sat, 22 Feb 2025 12:47:15 +0530 Subject: [PATCH 07/10] update readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 55a29b4..3fa41c2 100644 --- a/README.md +++ b/README.md @@ -67,8 +67,8 @@ torchaudio.save("sample.wav", wavs[0], model.autoencoder.sampling_rate) ### Gradio interface (recommended) ```bash -uv run gradio_interface.py -# python gradio_interface.py +uv run gradio_interface/main.py +# python gradio_interface/main.py ``` This should produce a `sample.wav` file in your project root directory. From 94efa553267747f3bf15edc4ab7b44bb92586658 Mon Sep 17 00:00:00 2001 From: xdevfaheem Date: Sat, 22 Feb 2025 14:27:54 +0530 Subject: [PATCH 08/10] no streaming, accumulation+concat insteadg as progress bar takes up streamed audio box (preventing access), it feel better to show the generated audio finally all at once --- README.md | 3 +-- gradio_interface/main.py | 23 ++++++++++++++--------- 2 files changed, 15 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 3fa41c2..12a822e 100644 --- a/README.md +++ b/README.md @@ -79,12 +79,11 @@ _For repeated sampling we highly recommend using the gradio interface instead, a - Zero-shot TTS with voice cloning: Input desired text and a 10-30s speaker sample to generate high quality TTS output - Text/File inputs: Support both written text and document (TXT, PDF, XLSX, DOCX and other [Docling](https://ds4sd.github.io/docling/supported_formats/) supported formats) -- Unlimited content: Support infinite content length due to the chunked generation + streaming +- Unlimited content: Support infinite content length due to the chunked generation - Audio prefix inputs: Add text plus an audio prefix for even richer speaker matching. Audio prefixes can be used to elicit behaviours such as whispering which can otherwise be challenging to replicate when cloning from speaker embeddings - Multilingual support: Zonos-v0.1 supports English, Japanese, Chinese, French, and German - Audio quality and emotion control: Zonos offers fine-grained control of many aspects of the generated audio. These include speaking rate, pitch, maximum frequency, audio quality, and various emotions such as happiness, anger, sadness, and fear. - Fast: our model runs with a real-time factor of ~2x on an RTX 4090 (i.e. generates 2 seconds of audio per 1 second of compute time) -- Streaming: Get the audio (of each text chunk) as soon as it is generated - Gradio WebUI: Zonos comes packaged with an easy to use gradio interface to generate speech - Simple installation and deployment: Zonos can be installed and deployed simply using the docker file packaged with our repository. diff --git a/gradio_interface/main.py b/gradio_interface/main.py index c961cb9..2f42a8a 100644 --- a/gradio_interface/main.py +++ b/gradio_interface/main.py @@ -3,7 +3,7 @@ import torchaudio import gradio as gr from os import getenv -from utils import process_file, float32_to_int16 +from utils import process_file, float32_to_int16, concatenate_audio from zonos.model import Zonos, DEFAULT_BACKBONE_CLS as ZonosBackbone from zonos.conditioning import make_cond_dict, supported_language_codes from zonos.utils import DEFAULT_DEVICE as device @@ -187,6 +187,7 @@ def generate_audio( total_chunks = len(text_chunks) gr.Info(f"Text split into {total_chunks} chunks. Starting synthesis...") + audio_segments = [] # Chunked Generation (unlimited content, yay!) for idx, chunk in enumerate(text_chunks, 1): if not chunk.strip(): @@ -237,16 +238,20 @@ def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool: if wav_out.dim() == 2 and wav_out.size(0) > 1: wav_out = wav_out[0:1, :] - if idx != total_chunks: - # add sub-sec silence between chunks - silence = torch.zeros(1, int(sr_out * 0.3)) - wav_out = torch.cat([wav_out, silence], dim=1) - # gradio audio streaming except 16bit int array - wav_array = float32_to_int16(wav_out.squeeze().numpy()) - yield (sr_out, wav_array), seed + audio_segments.append(wav_out) + if idx != total_chunks: # add sub-second silence after every audio chunk except last one + audio_segments.append(torch.zeros(1, int(sr_out * 0.2))) # 0.2 seem to be perfect + + if audio_segments: # most probably + final_audio = torch.cat(audio_segments, dim=1) # 1, k + wav_array = float32_to_int16(final_audio.squeeze().numpy()) # gradio audio except 16bit int array + return (sr_out, wav_array), seed + else: + return (None, None), seed except Exception as e: gr.Error(str(e)) + return (None, None), seed def build_interface(): @@ -379,7 +384,7 @@ def build_interface(): with gr.Column(): generate_button = gr.Button("Generate Audio") - output_audio = gr.Audio(label="Generated Audio", type="numpy", streaming=True, autoplay=True) + output_audio = gr.Audio(label="Generated Audio", type="numpy", autoplay=True) model_choice.change( fn=update_ui, From 5ab6c21fca0754f99a7b5cba649599f24b8fc8ef Mon Sep 17 00:00:00 2001 From: Mohammed Faheem Date: Sat, 22 Feb 2025 20:09:52 +0530 Subject: [PATCH 09/10] cute little bug --- gradio_interface/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gradio_interface/main.py b/gradio_interface/main.py index 2f42a8a..7f2ad22 100644 --- a/gradio_interface/main.py +++ b/gradio_interface/main.py @@ -3,7 +3,7 @@ import torchaudio import gradio as gr from os import getenv -from utils import process_file, float32_to_int16, concatenate_audio +from utils import process_file, float32_to_int16 from zonos.model import Zonos, DEFAULT_BACKBONE_CLS as ZonosBackbone from zonos.conditioning import make_cond_dict, supported_language_codes from zonos.utils import DEFAULT_DEVICE as device From 3b2b1479b14baf2f2ee05f3ef208a326093cc276 Mon Sep 17 00:00:00 2001 From: xdevfaheem Date: Sun, 23 Feb 2025 21:36:30 +0530 Subject: [PATCH 10/10] improved transition between audio chunks used asr for word timestamp to be used to get few word from previous text chunk and it's appropriate waveform to generate prefix codes (and cut that out later). So? probably, smoother and consistent transition with similarish vocalisation --- gradio_interface/main.py | 78 +++++++++++++++--- pyproject.toml | 1 + uv.lock | 167 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 233 insertions(+), 13 deletions(-) diff --git a/gradio_interface/main.py b/gradio_interface/main.py index 7f2ad22..f142075 100644 --- a/gradio_interface/main.py +++ b/gradio_interface/main.py @@ -1,4 +1,5 @@ -import tempfile +import tempfile, gc +import traceback import torch import torchaudio import gradio as gr @@ -7,9 +8,11 @@ from zonos.model import Zonos, DEFAULT_BACKBONE_CLS as ZonosBackbone from zonos.conditioning import make_cond_dict, supported_language_codes from zonos.utils import DEFAULT_DEVICE as device +from faster_whisper import WhisperModel CURRENT_MODEL_TYPE = None CURRENT_MODEL = None +ASR_MODEL = WhisperModel("small", device=device.type, compute_type="float16") SPEAKER_EMBEDDING = None SPEAKER_AUDIO_PATH = None @@ -20,6 +23,7 @@ def load_model_if_needed(model_choice: str): if CURRENT_MODEL_TYPE != model_choice: if CURRENT_MODEL is not None: del CURRENT_MODEL + gc.collect() torch.cuda.empty_cache() print(f"Loading {model_choice} model...") CURRENT_MODEL = Zonos.from_pretrained(model_choice, device=device) @@ -164,20 +168,13 @@ def generate_audio( if speaker_audio is not None and "speaker" not in unconditional_keys: if speaker_audio != SPEAKER_AUDIO_PATH: + print(speaker_audio, SPEAKER_AUDIO_PATH) print("Recomputed speaker embedding") wav, sr = torchaudio.load(speaker_audio) SPEAKER_EMBEDDING = selected_model.make_speaker_embedding(wav, sr) SPEAKER_EMBEDDING = SPEAKER_EMBEDDING.to(device, dtype=torch.bfloat16) SPEAKER_AUDIO_PATH = speaker_audio - audio_prefix_codes = None - if prefix_audio is not None: - wav_prefix, sr_prefix = torchaudio.load(prefix_audio) - wav_prefix = wav_prefix.mean(0, keepdim=True) - wav_prefix = selected_model.autoencoder.preprocess(wav_prefix, sr_prefix) - wav_prefix = wav_prefix.to(device, dtype=torch.float32) - audio_prefix_codes = selected_model.autoencoder.encode(wav_prefix.unsqueeze(0)) - emotion_tensor = torch.tensor(list(map(float, [e1, e2, e3, e4, e5, e6, e7, e8])), device=device) vq_val = float(vq_single) @@ -201,6 +198,53 @@ def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool: progress((step, estimated_total_steps)) return True + print("Computing Audio Prefix....") + if idx == 1 and not audio_segments: # for the first step, get the audio codes from provided prefix audio + audio_prefix_codes = None + if prefix_audio is not None: + wav_prefix, sr_prefix = torchaudio.load(prefix_audio) + wav_prefix = wav_prefix.mean(0, keepdim=True) + wav_prefix = selected_model.autoencoder.preprocess(wav_prefix, sr_prefix) + wav_prefix = wav_prefix.to(device, dtype=torch.float32) + audio_prefix_codes = selected_model.autoencoder.encode(wav_prefix.unsqueeze(0)) + else: + # asr word level timestamps + torchaudio.save("output.wav", audio_segments[-1], sample_rate=sr_out, format="wav") + segments, info = ASR_MODEL.transcribe( + "output.wav", + beam_size=5, + word_timestamps=True, + vad_filter=True, + vad_parameters=dict(min_silence_duration_ms=500), + ) + os.remove("output.wav") + word_segments = [word for segment in segments for word in segment.words] + n_last_words = word_segments[-4:] # last 4 words + print("Doing ASR...") + start_time = n_last_words[0].start + end_time = n_last_words[-1].end + transcript_prefix_text = "".join([each_word.word for each_word in n_last_words]) + # prefix transcription to the chunk + chunk = transcript_prefix_text + chunk + + start_sample = int(start_time * sr_out) + # slice out the samples belongs to the text + prev_wav_out_sliced = audio_segments[-1][ + :, start_sample: + ] # up till last sample, coz the sample from start to end bolongs to the text above + + # skip mean step as we already have a tensor of 1, prev_wav_out_sliced + wav_prefix = selected_model.autoencoder.preprocess(prev_wav_out_sliced, sr_out) + wav_prefix = wav_prefix.to(device, dtype=torch.float32) + audio_prefix_codes = selected_model.autoencoder.encode(wav_prefix.unsqueeze(0)) + + # so here basically, we extracted the approx samples belog to the last 4 words, + # and we also added the those last 4 words to the chunk + # now the current generation got a slice of text and it's waveform sample ss prefix + + print("Audio prefix codes shape:", audio_prefix_codes.shape) + print("Audio prefix codes:", audio_prefix_codes) + cond_dict = make_cond_dict( text=chunk.strip(), language=language, @@ -216,6 +260,7 @@ def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool: unconditional_keys=unconditional_keys, ) conditioning = selected_model.prepare_conditioning(cond_dict) + sr_out = selected_model.autoencoder.sampling_rate codes = selected_model.generate( prefix_conditioning=conditioning, @@ -228,9 +273,17 @@ def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool: ), callback=update_progress, ) + print("Codes shape:", codes.shape) + print("Codes:", codes) + # if idx!=1: codes = codes[..., audio_prefix_codes.shape[-1]:] # skip prefix codes wav_out = selected_model.autoencoder.decode(codes).cpu().detach() - sr_out = selected_model.autoencoder.sampling_rate + if idx != 1: + wav_out = wav_out[..., prev_wav_out_sliced.shape[-1] :] # skip prefix codes + + print("Wav_out shape:", wav_out.shape) + print("Wav_out:", wav_out) + if wav_out.dim() == 3: wav_out = wav_out.squeeze(0) if wav_out.dim() == 1: @@ -239,8 +292,6 @@ def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool: wav_out = wav_out[0:1, :] audio_segments.append(wav_out) - if idx != total_chunks: # add sub-second silence after every audio chunk except last one - audio_segments.append(torch.zeros(1, int(sr_out * 0.2))) # 0.2 seem to be perfect if audio_segments: # most probably final_audio = torch.cat(audio_segments, dim=1) # 1, k @@ -250,6 +301,7 @@ def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool: return (None, None), seed except Exception as e: + traceback.print_exc() gr.Error(str(e)) return (None, None), seed @@ -293,7 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