fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import argparse
import logging
import os
import csv
import tempfile
from collections import defaultdict
from pathlib import Path

import torchaudio
try:
    import webrtcvad
except ImportError:
    raise ImportError("Please install py-webrtcvad: pip install webrtcvad")
import pandas as pd
from tqdm import tqdm

from examples.speech_synthesis.preprocessing.denoiser.pretrained import master64
import examples.speech_synthesis.preprocessing.denoiser.utils as utils
from examples.speech_synthesis.preprocessing.vad import (
    frame_generator, vad_collector, read_wave, write_wave, FS_MS, THRESHOLD,
    SCALE
)
from examples.speech_to_text.data_utils import save_df_to_tsv


log = logging.getLogger(__name__)

PATHS = ["after_denoise", "after_vad"]
MIN_T = 0.05


def generate_tmp_filename(extension="txt"):
    return tempfile._get_default_tempdir() + "/" + \
           next(tempfile._get_candidate_names()) + "." + extension


def convert_sr(inpath, sr, output_path=None):
    if not output_path:
        output_path = generate_tmp_filename("wav")
    cmd = f"sox {inpath} -r {sr} {output_path}"
    os.system(cmd)
    return output_path


def apply_vad(vad, inpath):
    audio, sample_rate = read_wave(inpath)
    frames = frame_generator(FS_MS, audio, sample_rate)
    frames = list(frames)
    segments = vad_collector(sample_rate, FS_MS, 300, vad, frames)
    merge_segments = list()
    timestamp_start = 0.0
    timestamp_end = 0.0
    # removing start, end, and long sequences of sils
    for i, segment in enumerate(segments):
        merge_segments.append(segment[0])
        if i and timestamp_start:
            sil_duration = segment[1] - timestamp_end
            if sil_duration > THRESHOLD:
                merge_segments.append(int(THRESHOLD / SCALE) * (b'\x00'))
            else:
                merge_segments.append(int((sil_duration / SCALE)) * (b'\x00'))
        timestamp_start = segment[1]
        timestamp_end = segment[2]
    segment = b''.join(merge_segments)
    return segment, sample_rate


def write(wav, filename, sr=16_000):
    # Normalize audio if it prevents clipping
    wav = wav / max(wav.abs().max().item(), 1)
    torchaudio.save(filename, wav.cpu(), sr, encoding="PCM_S",
                    bits_per_sample=16)


def process(args):
    # making sure we are requested either denoise or vad
    if not args.denoise and not args.vad:
        log.error("No denoise or vad is requested.")
        return

    log.info("Creating out directories...")
    if args.denoise:
        out_denoise = Path(args.output_dir).absolute().joinpath(PATHS[0])
        out_denoise.mkdir(parents=True, exist_ok=True)
    if args.vad:
        out_vad = Path(args.output_dir).absolute().joinpath(PATHS[1])
        out_vad.mkdir(parents=True, exist_ok=True)

    log.info("Loading pre-trained speech enhancement model...")
    model = master64().to(args.device)

    log.info("Building the VAD model...")
    vad = webrtcvad.Vad(int(args.vad_agg_level))

    # preparing the output dict
    output_dict = defaultdict(list)

    log.info(f"Parsing input manifest: {args.audio_manifest}")
    with open(args.audio_manifest, "r") as f:
        manifest_dict = csv.DictReader(f, delimiter="\t")
        for row in tqdm(manifest_dict):
            filename = str(row["audio"])

            final_output = filename
            keep_sample = True
            n_frames = row["n_frames"]
            snr = -1
            if args.denoise:
                output_path_denoise = out_denoise.joinpath(Path(filename).name)
                # convert to 16khz in case we use a differet sr
                tmp_path = convert_sr(final_output, 16000)

                # loading audio file and generating the enhanced version
                out, sr = torchaudio.load(tmp_path)
                out = out.to(args.device)
                estimate = model(out)
                estimate = (1 - args.dry_wet) * estimate + args.dry_wet * out
                write(estimate[0], str(output_path_denoise), sr)

                snr = utils.cal_snr(out, estimate)
                snr = snr.cpu().detach().numpy()[0][0]
                final_output = str(output_path_denoise)

            if args.vad:
                output_path_vad = out_vad.joinpath(Path(filename).name)
                sr = torchaudio.info(final_output).sample_rate
                if sr in [16000, 32000, 48000]:
                    tmp_path = final_output
                elif sr < 16000:
                    tmp_path = convert_sr(final_output, 16000)
                elif sr < 32000:
                    tmp_path = convert_sr(final_output, 32000)
                else:
                    tmp_path = convert_sr(final_output, 48000)
                # apply VAD
                segment, sample_rate = apply_vad(vad, tmp_path)
                if len(segment) < sample_rate * MIN_T:
                    keep_sample = False
                    print((
                        f"WARNING: skip {filename} because it is too short "
                        f"after VAD ({len(segment) / sample_rate} < {MIN_T})"
                    ))
                else:
                    if sample_rate != sr:
                        tmp_path = generate_tmp_filename("wav")
                        write_wave(tmp_path, segment, sample_rate)
                        convert_sr(tmp_path, sr,
                                   output_path=str(output_path_vad))
                    else:
                        write_wave(str(output_path_vad), segment, sample_rate)
                    final_output = str(output_path_vad)
                    segment, _ = torchaudio.load(final_output)
                    n_frames = segment.size(1)

            if keep_sample:
                output_dict["id"].append(row["id"])
                output_dict["audio"].append(final_output)
                output_dict["n_frames"].append(n_frames)
                output_dict["tgt_text"].append(row["tgt_text"])
                output_dict["speaker"].append(row["speaker"])
                output_dict["src_text"].append(row["src_text"])
                output_dict["snr"].append(snr)

        out_tsv_path = Path(args.output_dir) / Path(args.audio_manifest).name
        log.info(f"Saving manifest to {out_tsv_path.as_posix()}")
        save_df_to_tsv(pd.DataFrame.from_dict(output_dict), out_tsv_path)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--audio-manifest", "-i", required=True,
                        type=str, help="path to the input manifest.")
    parser.add_argument(
        "--output-dir", "-o", required=True, type=str,
        help="path to the output dir. it will contain files after denoising and"
             " vad"
    )
    parser.add_argument("--vad-agg-level", "-a", type=int, default=2,
                        help="the aggresive level of the vad [0-3].")
    parser.add_argument(
        "--dry-wet", "-dw", type=float, default=0.01,
        help="the level of linear interpolation between noisy and enhanced "
             "files."
    )
    parser.add_argument(
        "--device", "-d", type=str, default="cpu",
        help="the device to be used for the speech enhancement model: "
             "cpu | cuda."
    )
    parser.add_argument("--denoise", action="store_true",
                        help="apply a denoising")
    parser.add_argument("--vad", action="store_true", help="apply a VAD")
    args = parser.parse_args()

    process(args)


if __name__ == "__main__":
    main()