Upload audio_utils.py with huggingface_hub

audio_utils.py

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+"""Audio preparation helpers for DreamVoice reference clips."""
+
+from __future__ import annotations
+
+import os
+import tempfile
+
+import librosa
+import numpy as np
+import soundfile as sf
+
+REFERENCE_SAMPLE_RATE = 16_000
+MIN_REFERENCE_SECONDS = 5.0
+MAX_REFERENCE_SECONDS = 60.0
+MIN_RMS = 0.005
+
+
+def prepare_reference(path: str) -> str:
+    """Clean and validate a voice reference clip for VoxCPM2.
+
+    The returned file is a temporary mono 16 kHz WAV. The caller owns cleanup of
+    that returned path after synthesis finishes.
+    """
+    if not path or not os.path.exists(path):
+        raise ValueError("Please record or upload a voice clip first.")
+
+    try:
+        audio, _ = librosa.load(path, sr=REFERENCE_SAMPLE_RATE, mono=True)
+    except Exception as exc:  # noqa: BLE001 - present a friendly UI-safe error.
+        raise ValueError("I couldn't read that audio clip. Please try a WAV or MP3 recording.") from exc
+
+    if audio.size == 0 or not np.isfinite(audio).all():
+        raise ValueError("That voice clip looks empty. Please record 5-60 seconds of clear speech.")
+
+    audio = np.asarray(audio, dtype=np.float32)
+    audio, _ = librosa.effects.trim(audio, top_db=35)
+
+    if audio.size == 0:
+        raise ValueError("That voice clip is too quiet. Please record closer to the microphone.")
+
+    peak = float(np.max(np.abs(audio)))
+    rms = float(np.sqrt(np.mean(np.square(audio))))
+    if peak <= 0.0 or rms < MIN_RMS:
+        raise ValueError("That voice clip is too quiet. Please record in a quiet room, closer to the microphone.")
+
+    duration = audio.size / REFERENCE_SAMPLE_RATE
+    if duration < MIN_REFERENCE_SECONDS:
+        raise ValueError("Please record at least 5 seconds of clear speech.")
+    if duration > MAX_REFERENCE_SECONDS:
+        max_samples = int(MAX_REFERENCE_SECONDS * REFERENCE_SAMPLE_RATE)
+        audio = audio[:max_samples]
+
+    audio = np.clip(audio, -1.0, 1.0)
+    fd, cleaned_path = tempfile.mkstemp(prefix="dreamvoice_ref_", suffix=".wav")
+    os.close(fd)
+    sf.write(cleaned_path, audio, REFERENCE_SAMPLE_RATE, subtype="PCM_16")
+    return cleaned_path