Add logic to create the preloaded audio samples for the website

Author: mitchsayre

.gitignore

@@ -1,4 +1,5 @@
 .venv*/
+out/
 
 __pycache__/
 build/

tests/test_basic.py

@@ -1,4 +1,6 @@
 import unittest
+import json
+from pathlib import Path
 
 try:
     import wfloat
@@ -52,6 +54,31 @@ def test_version_matches_expected(self) -> None:
 
     import wfloat
 
+    print(wfloat.version)
+
+    SPEAKER_IDS = {
+        "skilled_hero_man": 0,
+        "skilled_hero_woman": 1,
+        "fun_hero_man": 2,
+        "fun_hero_woman": 3,
+        "strong_hero_man": 4,
+        "strong_hero_woman": 5,
+        "mad_scientist_man": 6,
+        "mad_scientist_woman": 7,
+        "clever_villain_man": 8,
+        "clever_villain_woman": 9,
+        "narrator_man": 10,
+        "narrator_woman": 11,
+        "wise_elder_man": 12,
+        "wise_elder_woman": 13,
+        "outgoing_anime_man": 14,
+        "outgoing_anime_woman": 15,
+        "scary_villain_man": 16,
+        "scary_villain_woman": 17,
+        "news_reporter_man": 18,
+        "news_reporter_woman": 19,
+    }
+
     model = wfloat.OfflineTtsWfloatModelConfig(
         model="../wfloat-web/assets/models/wfloat-model/1.0.0/wfloat-model-1.0.0.onnx",
         tokens="../wfloat-web/assets/models/wfloat-model/1.0.0/wfloat-model-1.0.0_tokens.txt",
@@ -71,9 +98,78 @@ def test_version_matches_expected(self) -> None:
     )
 
     tts = wfloat.OfflineTts(config)
-    audio = tts.generate("Hello world.", sid=0, speed=1.0)
-    ok = wfloat.write_wave("out.wav", audio.samples, audio.sample_rate)
 
-    print("sample_rate:", audio.sample_rate)
-    print("num_samples:", len(audio.samples))
-    print("write_wave:", ok)
+    voices_path = "../../web/assets/js/voices.js"  # string path to the JSON file
+    with open(voices_path, "r", encoding="utf-8") as f:
+        voices_text = f.read()
+    prefix = "export const VOICES = "
+    voices_text = voices_text[len(prefix) :]
+
+    voices = json.loads(voices_text)
+    out_dir = Path("out")
+    out_dir.mkdir(exist_ok=True)
+    progress_by_voice = {}
+
+    for v in voices:
+        sid = SPEAKER_IDS[v["voiceId"]]
+        silence_padding_sec = v["padding"]
+        speed = v["speed"]
+        final_samples = []
+        sample_rate = None
+        raw_text_cursor = 0
+        current_time_sec = 0.0
+        progress_events = []
+
+        prepared = tts.prepare_wfloat_text(
+            v["text"],
+            emotion=v["emotion"],
+            intensity=v["intensity"],
+        )
+
+        for i in range(len(prepared.text)):
+            audio = tts.generate(prepared.text_clean[i], sid=sid, speed=speed)
+            if sample_rate is None:
+                sample_rate = audio.sample_rate
+            elif sample_rate != audio.sample_rate:
+                raise ValueError(
+                    f"Sample rate changed for {v['voiceId']}: "
+                    f"{sample_rate} != {audio.sample_rate}"
+                )
+
+            raw_chunk_text = prepared.text[i] or ""
+            highlight_start = raw_text_cursor
+            highlight_end = raw_text_cursor + len(raw_chunk_text)
+            raw_text_cursor = highlight_end
+
+            chunk_duration_sec = len(audio.samples) / sample_rate
+            padding_sec = silence_padding_sec if i < len(prepared.text) - 1 else 0.0
+            start_time_sec = current_time_sec
+            end_time_sec = start_time_sec + chunk_duration_sec + padding_sec
+
+            progress_events.append(
+                {
+                    "text": raw_chunk_text,
+                    "progress": (i + 1) / len(prepared.text),
+                    "textHighlightStart": highlight_start,
+                    "textHighlightEnd": highlight_end,
+                    "startTimeSec": start_time_sec,
+                    "endTimeSec": end_time_sec,
+                }
+            )
+
+            final_samples.extend(audio.samples)
+            current_time_sec += chunk_duration_sec
+
+            if i < len(prepared.text) - 1:
+                silence_samples = int(sample_rate * silence_padding_sec)
+                final_samples.extend([0] * silence_samples)
+                current_time_sec += silence_padding_sec
+
+        output_path = out_dir / f"{v['voiceId']}.wav"
+        ok = wfloat.write_wave(str(output_path), final_samples, sample_rate)
+        progress_by_voice[v["voiceId"]] = progress_events
+        print(f"{output_path}: sample_rate={sample_rate} num_samples={len(final_samples)} write_wave={ok}")
+
+    progress_path = out_dir / "progress.json"
+    with open(progress_path, "w", encoding="utf-8") as f:
+        json.dump(progress_by_voice, f, ensure_ascii=False, indent=2)