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Add filter utterances
This commit is contained in:
Michael Hansen
committed by
Michael Hansen
Michael Hansen
parent
10b136cdf8
commit
5a64768924
266
src/python/piper_train/filter_utterances.py
Normal file
266
src/python/piper_train/filter_utterances.py
Normal file
@@ -0,0 +1,266 @@
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#!/usr/bin/env python3
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import argparse
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import csv
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import json
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import re
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import sys
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import statistics
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import shutil
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import subprocess
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import threading
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from collections import defaultdict
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from concurrent.futures import ThreadPoolExecutor
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from dataclasses import asdict, dataclass
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from enum import Enum
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from pathlib import Path
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from typing import Optional
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import numpy as np
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from .norm_audio import make_silence_detector, trim_silence, SileroVoiceActivityDetector
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_DIR = Path(__file__).parent
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# Removed from the speaking rate calculation
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_PUNCTUATION = re.compile(".。,，?¿？؟!！;；:：-—")
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class ExcludeReason(str, Enum):
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MISSING = "file_missing"
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EMPTY = "file_empty"
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LOW = "rate_low"
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HIGH = "rate_high"
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@dataclass
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class Utterance:
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id: str
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text: str
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duration_sec: float
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speaker: str
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exclude_reason: Optional[ExcludeReason] = None
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rate: float = 0.0
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def __post_init__(self):
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if self.duration_sec > 0:
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# Don't include punctuation is speaking rate calculation since we
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# remove silence.
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text_nopunct = _PUNCTUATION.sub("", self.text)
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self.rate = len(text_nopunct) / self.duration_sec
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--write-json", help="Path to write information about excluded utterances"
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)
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parser.add_argument(
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"--dataset-dir", default=Path.cwd(), help="Path to dataset directory"
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)
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parser.add_argument("--scale-lower", type=float, default=2.0)
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parser.add_argument("--scale-upper", type=float, default=2.0)
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args = parser.parse_args()
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if not shutil.which("ffprobe"):
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raise RuntimeError("ffprobe not found (is ffmpeg installed?)")
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dataset_dir = Path(args.dataset_dir)
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wav_dir = dataset_dir / "wav"
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if not wav_dir.is_dir():
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wav_dir = dataset_dir / "wavs"
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reader = csv.reader(sys.stdin, delimiter="|")
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text_and_audio = []
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for row in reader:
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filename, text = row[0], row[-1]
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speaker = row[1] if len(row) > 2 else "default"
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# Try file name relative to metadata
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wav_path = dataset_dir / filename
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if not wav_path.exists():
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# Try with .wav
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wav_path = dataset_dir / f"{filename}.wav"
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if not wav_path.exists():
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# Try wav/ or wavs/
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wav_path = wav_dir / filename
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if not wav_path.exists():
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# Try with .wav
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wav_path = wav_dir / f"{filename}.wav"
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text_and_audio.append((filename, text, wav_path, speaker))
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writer = csv.writer(sys.stdout, delimiter="|")
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# speaker -> [rate]
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utts_by_speaker = defaultdict(list)
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process_utterance = ProcessUtterance()
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with ThreadPoolExecutor() as executor:
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for utt in executor.map(lambda args: process_utterance(*args), text_and_audio):
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utts_by_speaker[utt.speaker].append(utt)
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is_multispeaker = len(utts_by_speaker) > 1
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writer = csv.writer(sys.stdout, delimiter="|")
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speaker_details = {}
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for speaker, utts in utts_by_speaker.items():
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rates = [utt.rate for utt in utts]
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if rates:
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# Exclude rates well outside the 25%/75% quantiles
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rate_qs = statistics.quantiles(rates, n=4)
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q1 = rate_qs[0] # 25%
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q3 = rate_qs[-1] # 75%
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iqr = q3 - q1
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lower = q1 - (args.scale_lower * iqr)
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upper = q3 + (args.scale_upper * iqr)
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speaker_details[speaker] = {
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"min": min(rates),
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"max": max(rates),
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"quanties": rate_qs,
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"lower": lower,
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"upper": upper,
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}
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for utt in utts:
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if utt.rate < lower:
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utt.exclude_reason = ExcludeReason.LOW
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elif utt.rate > upper:
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utt.exclude_reason = ExcludeReason.HIGH
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else:
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if is_multispeaker:
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writer.writerow((utt.id, utt.text, utt.speaker))
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else:
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writer.writerow((utt.id, utt.text))
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if args.write_json:
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speaker_excluded = {
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speaker: [
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asdict(utt)
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for utt in utts_by_speaker[speaker]
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if utt.exclude_reason is not None
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]
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for speaker in speaker_details
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}
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with open(args.write_json, "w") as json_file:
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json.dump(
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{
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speaker: {
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"details": speaker_details[speaker],
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"num_utterances": len(utts_by_speaker[speaker]),
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"num_excluded": len(speaker_excluded[speaker]),
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"excluded": speaker_excluded[speaker],
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}
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for speaker in speaker_details
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},
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json_file,
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indent=4,
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)
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class ProcessUtterance:
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def __init__(self):
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self.thread_data = threading.local()
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def __call__(
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self, utt_id: str, text: str, wav_path: Path, speaker: str
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) -> Utterance:
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if not wav_path.exists():
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return Utterance(
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utt_id,
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text,
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0.0,
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speaker,
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exclude_reason=ExcludeReason.MISSING,
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)
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if wav_path.stat().st_size == 0:
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return Utterance(
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utt_id,
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text,
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0.0,
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speaker,
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exclude_reason=ExcludeReason.EMPTY,
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)
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return Utterance(utt_id, text, self.get_duration(wav_path), speaker)
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def get_duration(self, audio_path: Path) -> float:
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"""Uses ffmpeg to get audio duration."""
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if not hasattr(self.thread_data, "detector"):
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self.thread_data.detector = make_silence_detector()
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vad_sample_rate = 16000
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audio_16khz_bytes = subprocess.check_output(
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[
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"ffmpeg",
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"-i",
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str(audio_path),
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"-f",
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"s16le",
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"-acodec",
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"pcm_s16le",
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"-ac",
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"1",
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"-ar",
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str(vad_sample_rate),
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"pipe:",
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],
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stderr=subprocess.DEVNULL,
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)
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# Normalize
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audio_16khz = np.frombuffer(audio_16khz_bytes, dtype=np.int16).astype(
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np.float32
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)
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audio_16khz /= np.abs(np.max(audio_16khz))
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# Get speaking duration
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offset_sec, duration_sec = trim_silence(
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audio_16khz,
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self.thread_data.detector,
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threshold=0.5,
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samples_per_chunk=480,
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sample_rate=vad_sample_rate,
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keep_chunks_before=2,
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keep_chunks_after=2,
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)
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if duration_sec is None:
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# Speech goes to end of audio
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if len(audio_16khz) > 0:
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duration_sec = (len(audio_16khz) / 16000.0) - offset_sec
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else:
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duration_sec = 0.0
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return duration_sec
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# return float(
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# subprocess.check_output(
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# [
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# "ffprobe",
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# "-i",
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# str(audio_path),
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# "-show_entries",
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# "format=duration",
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# "-v",
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# "quiet",
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# "-of",
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# "csv=p=0",
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# ],
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# stderr=subprocess.DEVNULL,
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# universal_newlines=True,
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# ).strip()
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# )
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def make_silence_detector() -> SileroVoiceActivityDetector:
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silence_model = _DIR / "norm_audio" / "models" / "silero_vad.onnx"
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return SileroVoiceActivityDetector(silence_model)
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if __name__ == "__main__":
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main()
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@@ -1,72 +0,0 @@
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#!/usr/bin/env python3
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import argparse
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import csv
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import json
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import sys
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import statistics
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from pathlib import Path
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import librosa
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--dataset_dir", default=Path.cwd())
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parser.add_argument("--csv", action="store_true")
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args = parser.parse_args()
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dataset_dir = Path(args.dataset_dir)
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wav_dir = dataset_dir / "wav"
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if not wav_dir.is_dir():
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wav_dir = dataset_dir / "wavs"
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reader = csv.reader(sys.stdin, delimiter="|")
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writer = csv.writer(sys.stdout, delimiter="|")
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rates = []
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for row in reader:
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filename, text = row[0], row[-1]
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# Try file name relative to metadata
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wav_path = dataset_dir / filename
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if not wav_path.exists():
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# Try with .wav
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wav_path = dataset_dir / f"{filename}.wav"
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if not wav_path.exists():
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# Try wav/ or wavs/
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wav_path = wav_dir / filename
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if not wav_path.exists():
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# Try with .wav
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wav_path = wav_dir / f"{filename}.wav"
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if not wav_path.exists():
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print("Missing", wav_path, file=sys.stderr)
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continue
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if wav_path.stat().st_size == 0:
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print("Empty", wav_path, file=sys.stderr)
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continue
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duration = librosa.get_duration(path=wav_path)
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rate = duration / len(text)
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if args.csv:
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writer.writerow((filename, text, duration, rate))
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else:
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rates.append(rate)
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if not args.csv:
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json.dump(
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{
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"rates": rates,
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"mean": statistics.mean(rates),
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"median": statistics.median(rates),
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},
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sys.stdout,
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)
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if __name__ == "__main__":
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main()
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@@ -11,7 +11,7 @@ from collections import Counter
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from dataclasses import dataclass, field
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from multiprocessing import JoinableQueue, Process, Queue
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from pathlib import Path
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from typing import Dict, Iterable, List, Optional
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from typing import Dict, Iterable, List, Optional, Tuple
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import librosa
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from espeak_phonemizer import Phonemizer
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@@ -71,13 +71,6 @@ def main() -> None:
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help="Casing applied to utterance text",
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)
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#
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parser.add_argument(
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"--speaking-rate-min", type=float, help="Minimum speaking rate (chars/sec)"
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)
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parser.add_argument(
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"--speaking-rate-max", type=float, help="Maximum speaking rate (chars/sec)"
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)
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#
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parser.add_argument(
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"--skip-audio", action="store_true", help="Don't preprocess audio"
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)
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@@ -355,32 +348,6 @@ class PathEncoder(json.JSONEncoder):
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return super().default(o)
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def is_good_speaking_rate(
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text: str,
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wav_path: Path,
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args: argparse.Namespace,
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) -> bool:
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min_rate: Optional[float] = args.speaking_rate_min
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max_rate: Optional[float] = args.speaking_rate_max
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if (min_rate is None) and (max_rate is None):
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return True
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if len(text) == 0:
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return False
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duration = librosa.get_duration(path=wav_path)
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rate = len(text) / duration
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if (min_rate is not None) and (rate < min_rate):
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return False
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if (max_rate is not None) and (rate > max_rate):
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return False
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return True
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def ljspeech_dataset(args: argparse.Namespace) -> Iterable[Utterance]:
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dataset_dir = args.input_dir
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is_single_speaker = args.single_speaker
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@@ -431,10 +398,6 @@ def ljspeech_dataset(args: argparse.Namespace) -> Iterable[Utterance]:
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_LOGGER.warning("Empty file: %s", wav_path)
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continue
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if not is_good_speaking_rate(text, wav_path, args):
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_LOGGER.warning("Bad speaking rate: %s", wav_path)
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continue
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yield Utterance(
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text=text, audio_path=wav_path, speaker=speaker, speaker_id=speaker_id
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)
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