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