Spencer Grimes
d0de47bdd7
- Replace Unicode emoji (✓, ⚠️) with [OK] and [WARN] in audio_preprocessor.py
to prevent UnicodeEncodeError on Windows console (cp1252 codec)
- Add auto-update dependencies function to bot.py for easier maintenance
- Remove setup_linux.sh (no longer needed)
- Update .gitignore to exclude VS Code launch.json
206 lines
6.7 KiB
Python
206 lines
6.7 KiB
Python
"""Audio preprocessing utilities for improving voice cloning quality."""
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import os
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import tempfile
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from dataclasses import dataclass
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import librosa
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import noisereduce as nr
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import numpy as np
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import soundfile as sf
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@dataclass
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class PreprocessingConfig:
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"""Configuration for audio preprocessing."""
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target_sample_rate: int = 22050
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normalize: bool = True
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trim_silence: bool = True
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trim_top_db: int = 20
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reduce_noise: bool = True
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target_length_seconds: float | None = None # None means keep original length
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class AudioPreprocessor:
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"""Preprocesses audio files for optimal voice cloning."""
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def __init__(self, config: PreprocessingConfig | None = None):
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self.config = config or PreprocessingConfig()
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def preprocess_file(self, input_path: str, output_path: str | None = None) -> str:
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"""
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Preprocess an audio file for voice cloning.
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Args:
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input_path: Path to the input audio file
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output_path: Optional path for the output file. If None, creates a temp file.
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Returns:
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Path to the preprocessed audio file
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"""
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print(f"Preprocessing audio: {input_path}")
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# Load audio with librosa (automatically converts to mono and resamples)
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audio, sr = librosa.load(
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input_path,
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sr=self.config.target_sample_rate,
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mono=True
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)
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print(f" Loaded audio: {len(audio) / sr:.2f}s at {sr}Hz")
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# Apply preprocessing steps
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audio = self._normalize(audio)
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audio = self._trim_silence(audio, sr)
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audio = self._reduce_noise(audio, sr)
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audio = self._limit_length(audio, sr)
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# Ensure we have valid audio
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if len(audio) < sr * 0.5: # Less than 0.5 seconds
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print(" Warning: Audio is very short after preprocessing!")
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# Save to output path
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if output_path is None:
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fd, output_path = tempfile.mkstemp(suffix=".wav")
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os.close(fd)
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sf.write(output_path, audio, sr, subtype="PCM_16")
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print(f" Saved preprocessed audio: {output_path} ({len(audio) / sr:.2f}s)")
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return output_path
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def preprocess_to_array(self, input_path: str) -> tuple[np.ndarray, int]:
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"""
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Preprocess an audio file and return as numpy array.
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Args:
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input_path: Path to the input audio file
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Returns:
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Tuple of (audio array, sample rate)
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"""
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# Use temporary file approach for consistency
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temp_path = self.preprocess_file(input_path)
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audio, sr = librosa.load(temp_path, sr=None, mono=True)
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os.unlink(temp_path)
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return audio, sr
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def _normalize(self, audio: np.ndarray) -> np.ndarray:
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"""Normalize audio to a consistent volume level."""
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if not self.config.normalize:
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return audio
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max_val = np.max(np.abs(audio))
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if max_val > 0:
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# Normalize to 95% of max to avoid clipping
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audio = audio / max_val * 0.95
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print(" Applied volume normalization")
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return audio
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def _trim_silence(self, audio: np.ndarray, sr: int) -> np.ndarray:
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"""Trim silence from the beginning and end of audio."""
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if not self.config.trim_silence:
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return audio
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trimmed, _ = librosa.effects.trim(
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audio,
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top_db=self.config.trim_top_db
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)
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trimmed_duration = len(audio) - len(trimmed)
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if trimmed_duration > 0:
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print(f" Trimmed {trimmed_duration / sr:.2f}s of silence")
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return trimmed
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def _reduce_noise(self, audio: np.ndarray, sr: int) -> np.ndarray:
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"""Apply noise reduction to the audio."""
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if not self.config.reduce_noise:
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return audio
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try:
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reduced = nr.reduce_noise(
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y=audio,
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sr=sr,
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stationary=True,
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prop_decrease=0.75
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)
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print(" Applied noise reduction")
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return reduced
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except Exception as e:
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print(f" Warning: Noise reduction failed: {e}")
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return audio
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def _limit_length(self, audio: np.ndarray, sr: int) -> np.ndarray:
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"""Limit audio to target length if specified."""
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if self.config.target_length_seconds is None:
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return audio
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max_samples = int(self.config.target_length_seconds * sr)
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if len(audio) > max_samples:
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audio = audio[:max_samples]
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print(f" Trimmed to {self.config.target_length_seconds}s")
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return audio
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def analyze_audio(file_path: str) -> dict:
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"""
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Analyze an audio file and return its properties.
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Useful for debugging voice cloning issues.
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"""
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audio, sr = librosa.load(file_path, sr=None, mono=False)
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is_stereo = audio.ndim > 1
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if is_stereo:
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audio_mono = librosa.to_mono(audio)
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else:
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audio_mono = audio
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duration = len(audio_mono) / sr
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max_amplitude = np.max(np.abs(audio_mono))
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rms = np.sqrt(np.mean(audio_mono**2))
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# Estimate noise level from quietest parts
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frame_length = int(sr * 0.025)
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hop_length = int(sr * 0.010)
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rms_frames = librosa.feature.rms(
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y=audio_mono,
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frame_length=frame_length,
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hop_length=hop_length
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)[0]
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noise_floor = np.percentile(rms_frames, 10)
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return {
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"path": file_path,
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"sample_rate": sr,
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"duration_seconds": duration,
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"is_stereo": is_stereo,
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"max_amplitude": float(max_amplitude),
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"rms_level": float(rms),
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"estimated_noise_floor": float(noise_floor),
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"is_normalized": max_amplitude > 0.8,
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"is_too_short": duration < 3,
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"is_too_long": duration > 30,
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"needs_resampling": sr != 22050,
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}
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def print_audio_analysis(file_path: str) -> None:
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"""Print a formatted analysis of an audio file."""
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info = analyze_audio(file_path)
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print(f"\n{'=' * 50}")
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print(f"Audio Analysis: {info['path']}")
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print(f"{'=' * 50}")
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print(f" Sample Rate: {info['sample_rate']} Hz {'[WARN] (should be 22050)' if info['needs_resampling'] else '[OK]'}")
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print(f" Duration: {info['duration_seconds']:.2f}s", end="")
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if info['is_too_short']:
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print(" [WARN] (too short, aim for 5-15s)")
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elif info['is_too_long']:
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print(" [WARN] (quite long, 5-15s is ideal)")
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else:
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print(" [OK]")
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print(f" Channels: {'Stereo' if info['is_stereo'] else 'Mono'} {'[WARN] (will convert to mono)' if info['is_stereo'] else '[OK]'}")
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print(f" Max Amplitude: {info['max_amplitude']:.3f} {'[OK]' if info['is_normalized'] else '[WARN] (low volume)'}")
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print(f" RMS Level: {info['rms_level']:.4f}")
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print(f" Noise Floor: {info['estimated_noise_floor']:.4f}")
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print(f"{'=' * 50}\n")
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