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# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/4A. S2A dataset preparation.ipynb.
# %% auto 0
__all__ = ['flac_to_s2a_name']
# %% ../nbs/4A. S2A dataset preparation.ipynb 2
import sys
import os
import itertools
from pathlib import Path
import numpy as np
import torch
import torchaudio
import torch.nn.functional as F
from torch.profiler import profile, record_function, ProfilerActivity
from fastprogress import progress_bar
from fastcore.script import *
import whisper
from . import vad, wh_transcribe, vq_stoks, extract_acoustic
import webdataset as wds
# %% ../nbs/4A. S2A dataset preparation.ipynb 4
def flac_to_s2a_name(input):
if '-flac-' in input:
return input.rsplit("/", 1)[1].replace('flac', 's2a') + ".gz"
else:
return input.rsplit("/", 1)[1].replace('raw', 's2a') + ".gz"
# %% ../nbs/4A. S2A dataset preparation.ipynb 6
def resampler(newsr = 24000, key = 'samples_24k'):
_last_sr = None
tform = None
def _resample(samples):
for s in samples:
sr = s['sample_rate']
if sr != newsr:
if sr != _last_sr: tform = torchaudio.transforms.Resample(sr, newsr)
s[key] = tform(s['samples'])
else:
s[key] = s['samples']
yield s
return _resample
# %% ../nbs/4A. S2A dataset preparation.ipynb 9
@call_parse
def prepare_s2a(
input:str, # FLAC webdataset file path (or - to read the names from stdin)
proc_dataset_path:Path, # processed VAD files path
output:str=None, # output file name
vq_model:str="collabora/spear-tts-pytorch:whisper-vq-stoks.model", # the model path (use repo_id:filename to download it from hugginface)
n_samples:int=None, # process a limited amount of samples
batch_size:int=1, # process several segments at once
fix_dots:bool=False, # fix dots in file names
):
if ":" in vq_model:
repo, fname = vq_model.split(":", 1)
vq_model = vq_stoks.RQBottleneckTransformer.load_model(repo, fname).cuda()
else:
vq_model = vq_stoks.RQBottleneckTransformer.load_model(local_filename=vq_model).cuda()
amodel = extract_acoustic.load_model()
amodel.set_target_bandwidth(3)
if input == "-":
input = [f.strip() for f in sys.stdin.readlines()]
assert output, "please provide the output shard name"
else:
if output is None: output = flac_to_s2a_name(input)
input = [input]
total = n_samples//batch_size if n_samples else 'noinfer'
ds = wds.WebDataset(input, shardshuffle=True, rename_files=vad.fix_dots_in_names if fix_dots else None).compose(
wds.decode(wds.torch_audio),
wds.select(lambda x: 'wav' in x or 'flac' in x),
vq_stoks.merge_in(vq_stoks.derived_dataset(proc_dataset_path, 'vad')),
wds.map_dict(**{"vad.npy":wh_transcribe.chunk_merger}),
lambda x: wh_transcribe.split_to_chunks(x),
resampler(),
resampler(16000, 'samples_16k'),
wds.to_tuple('__key__', 'rpad_s', 'samples_16k', 'samples_24k'),
wds.batched(64),
)
dl = wds.WebLoader(ds, num_workers=4, batch_size=None).unbatched().shuffle(2000).batched(batch_size)
speakers = set()
tmp = output+".tmp"
with wds.TarWriter(tmp) as sink:
for keys, rpad_ss, samples, samples24k in progress_bar(dl, total=total):
with record_function('to_cuda'):
samples, samples24k = samples.cuda(), samples24k.unsqueeze(1).cuda()
with record_function('encodec'):
atoks = amodel.encode(samples24k)[0][0]
with record_function('vq_stoks'):
stoks = vq_model.encode_audio(samples)
with record_function('from_cuda'):
atoks, stoks = atoks.cpu().numpy().astype(np.int16), stoks.cpu().numpy().astype(np.int16)
for key, rpad_s, _atoks, _stoks in zip(keys, rpad_ss, atoks, stoks):
speakers.add(key.split('/')[1])
sink.write({
"__key__": key,
"atoks.npy": _atoks[:,:int(-rpad_s * 75)],
"stoks.npy": _stoks[:int(-rpad_s * 25)],
})
with open(output+".speakers.txt", "w") as f: f.write("\n".join(speakers))
if not n_samples:
os.rename(tmp, output)
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