CosyVoice/cosyvoice/dataset/processor.py

# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import random

import pyarrow.parquet as pq
from io import BytesIO
import torch
import torchaudio
from torch.nn.utils.rnn import pad_sequence
import torch.nn.functional as F

torchaudio.set_audio_backend('soundfile')

AUDIO_FORMAT_SETS = set(['flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma'])


def parquet_opener(data, mode='train', tts_data={}):
    """ Give url or local file, return file descriptor
        Inplace operation.

        Args:
            data(Iterable[str]): url or local file list

        Returns:
            Iterable[{src, stream}]
    """
    for sample in data:
        assert 'src' in sample
        url = sample['src']
        try:
            df = pq.read_table(url).to_pandas()
            for i in range(len(df)):
                if mode == 'inference' and df.loc[i, 'utt'] not in tts_data:
                    continue
                sample.update(dict(df.loc[i]))
                if mode == 'train':
                    # NOTE do not return sample directly, must initialize a new dict
                    yield {**sample}
                else:
                    for index, text in enumerate(tts_data[df.loc[i, 'utt']]):
                        yield {**sample, 'tts_index': index, 'tts_text': text}
        except Exception as ex:
            logging.warning('Failed to open {}, ex info {}'.format(url, ex))

def filter(data,
           max_length=10240,
           min_length=10,
           token_max_length=200,
           token_min_length=1,
           min_output_input_ratio=0.0005,
           max_output_input_ratio=1,
           mode='train'):
    """ Filter sample according to feature and label length
        Inplace operation.

        Args::
            data: Iterable[{key, wav, label, sample_rate}]
            max_length: drop utterance which is greater than max_length(10ms)
            min_length: drop utterance which is less than min_length(10ms)
            token_max_length: drop utterance which is greater than
                token_max_length, especially when use char unit for
                english modeling
            token_min_length: drop utterance which is
                less than token_max_length
            min_output_input_ratio: minimal ration of
                token_length / feats_length(10ms)
            max_output_input_ratio: maximum ration of
                token_length / feats_length(10ms)

        Returns:
            Iterable[{key, wav, label, sample_rate}]
    """
    for sample in data:
        sample['speech'], sample['sample_rate'] = torchaudio.load(BytesIO(sample['audio_data']))
        del sample['audio_data']
        # sample['wav'] is torch.Tensor, we have 100 frames every second
        num_frames = sample['speech'].size(1) / sample['sample_rate'] * 100
        if num_frames < min_length:
            continue
        if num_frames > max_length:
            continue
        if len(sample['text_token']) < token_min_length:
            continue
        if len(sample['text_token']) > token_max_length:
            continue
        if len(sample['speech_token']) == 0:
            continue
        if num_frames != 0:
            if len(sample['text_token']) / num_frames < min_output_input_ratio:
                continue
            if len(sample['text_token']) / num_frames > max_output_input_ratio:
                continue
        yield sample


def resample(data, resample_rate=22050, min_sample_rate=16000, mode='train'):
    """ Resample data.
        Inplace operation.

        Args:
            data: Iterable[{key, wav, label, sample_rate}]
            resample_rate: target resample rate

        Returns:
            Iterable[{key, wav, label, sample_rate}]
    """
    for sample in data:
        assert 'sample_rate' in sample
        assert 'speech' in sample
        sample_rate = sample['sample_rate']
        waveform = sample['speech']
        if sample_rate != resample_rate:
            if sample_rate < min_sample_rate:
                continue
            sample['sample_rate'] = resample_rate
            sample['speech'] = torchaudio.transforms.Resample(
                orig_freq=sample_rate, new_freq=resample_rate)(waveform)
        max_val = sample['speech'].abs().max()
        if max_val > 1:
            sample['speech'] /= max_val
        yield sample


def compute_fbank(data,
                  feat_extractor,
                  mode='train'):
    """ Extract fbank

        Args:
            data: Iterable[{key, wav, label, sample_rate}]

        Returns:
            Iterable[{key, feat, label}]
    """
    for sample in data:
        assert 'sample_rate' in sample
        assert 'speech' in sample
        assert 'utt' in sample
        assert 'text_token' in sample
        waveform = sample['speech']
        mat = feat_extractor(waveform).squeeze(dim=0).transpose(0, 1)
        sample['speech_feat'] = mat
        del sample['speech']
        yield sample


def parse_embedding(data, normalize, mode='train'):
    """ Parse utt_embedding/spk_embedding

        Args:
            data: Iterable[{key, wav, label, sample_rate}]

        Returns:
            Iterable[{key, feat, label}]
    """
    for sample in data:
        sample['utt_embedding'] = torch.tensor(sample['utt_embedding'], dtype=torch.float32)
        sample['spk_embedding'] = torch.tensor(sample['spk_embedding'], dtype=torch.float32)
        if normalize:
            sample['utt_embedding'] = F.normalize(sample['utt_embedding'], dim=0)
            sample['spk_embedding'] = F.normalize(sample['spk_embedding'], dim=0)
        yield sample


def tokenize(data, get_tokenizer, allowed_special, mode='train'):
    """ Decode text to chars or BPE
        Inplace operation

        Args:
            data: Iterable[{key, wav, txt, sample_rate}]

        Returns:
            Iterable[{key, wav, txt, tokens, label, sample_rate}]
    """
    tokenizer = get_tokenizer()
    for sample in data:
        assert 'text' in sample
        sample['text_token'] = tokenizer.encode(sample['text'], allowed_special=allowed_special)
        if mode == 'inference':
            sample['tts_text_token'] = tokenizer.encode(sample['tts_text'], allowed_special=allowed_special)
        yield sample


def shuffle(data, shuffle_size=10000, mode='train'):
    """ Local shuffle the data

        Args:
            data: Iterable[{key, feat, label}]
            shuffle_size: buffer size for shuffle

        Returns:
            Iterable[{key, feat, label}]
    """
    buf = []
    for sample in data:
        buf.append(sample)
        if len(buf) >= shuffle_size:
            random.shuffle(buf)
            for x in buf:
                yield x
            buf = []
    # The sample left over
    random.shuffle(buf)
    for x in buf:
        yield x


def sort(data, sort_size=500, mode='train'):
    """ Sort the data by feature length.
        Sort is used after shuffle and before batch, so we can group
        utts with similar lengths into a batch, and `sort_size` should
        be less than `shuffle_size`

        Args:
            data: Iterable[{key, feat, label}]
            sort_size: buffer size for sort

        Returns:
            Iterable[{key, feat, label}]
    """

    buf = []
    for sample in data:
        buf.append(sample)
        if len(buf) >= sort_size:
            buf.sort(key=lambda x: x['speech_feat'].size(0))
            for x in buf:
                yield x
            buf = []
    # The sample left over
    buf.sort(key=lambda x: x['speech_feat'].size(0))
    for x in buf:
        yield x


def static_batch(data, batch_size=16):
    """ Static batch the data by `batch_size`

        Args:
            data: Iterable[{key, feat, label}]
            batch_size: batch size

        Returns:
            Iterable[List[{key, feat, label}]]
    """
    buf = []
    for sample in data:
        buf.append(sample)
        if len(buf) >= batch_size:
            yield buf
            buf = []
    if len(buf) > 0:
        yield buf


def dynamic_batch(data, max_frames_in_batch=12000, mode='train'):
    """ Dynamic batch the data until the total frames in batch
        reach `max_frames_in_batch`

        Args:
            data: Iterable[{key, feat, label}]
            max_frames_in_batch: max_frames in one batch

        Returns:
            Iterable[List[{key, feat, label}]]
    """
    buf = []
    longest_frames = 0
    for sample in data:
        assert 'speech_feat' in sample
        assert isinstance(sample['speech_feat'], torch.Tensor)
        new_sample_frames = sample['speech_feat'].size(0)
        longest_frames = max(longest_frames, new_sample_frames)
        frames_after_padding = longest_frames * (len(buf) + 1)
        if frames_after_padding > max_frames_in_batch:
            yield buf
            buf = [sample]
            longest_frames = new_sample_frames
        else:
            buf.append(sample)
    if len(buf) > 0:
        yield buf


def batch(data, batch_type='static', batch_size=16, max_frames_in_batch=12000, mode='train'):
    """ Wrapper for static/dynamic batch
    """
    if mode == 'inference':
        return static_batch(data, 1)
    else:
        if batch_type == 'static':
            return static_batch(data, batch_size)
        elif batch_type == 'dynamic':
            return dynamic_batch(data, max_frames_in_batch)
        else:
            logging.fatal('Unsupported batch type {}'.format(batch_type))


def padding(data, use_spk_embedding, mode='train'):
    """ Padding the data into training data

        Args:
            data: Iterable[List[{key, feat, label}]]

        Returns:
            Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)]
    """
    for sample in data:
        assert isinstance(sample, list)
        speech_feat_len = torch.tensor([x['speech_feat'].size(1) for x in sample],
                                       dtype=torch.int32)
        order = torch.argsort(speech_feat_len, descending=True)

        utts = [sample[i]['utt'] for i in order]
        speech_token = [torch.tensor(sample[i]['speech_token']) for i in order]
        speech_token_len = torch.tensor([i.size(0) for i in speech_token], dtype=torch.int32)
        speech_token = pad_sequence(speech_token,
                                    batch_first=True,
                                    padding_value=0)
        speech_feat = [sample[i]['speech_feat'] for i in order]
        speech_feat_len = torch.tensor([i.size(0) for i in speech_feat], dtype=torch.int32)
        speech_feat = pad_sequence(speech_feat,
                                   batch_first=True,
                                   padding_value=0)
        text = [sample[i]['text'] for i in order]
        text_token = [torch.tensor(sample[i]['text_token']) for i in order]
        text_token_len = torch.tensor([i.size(0) for i in text_token], dtype=torch.int32)
        text_token = pad_sequence(text_token, batch_first=True, padding_value=0)
        utt_embedding = torch.stack([sample[i]['utt_embedding'] for i in order], dim=0)
        spk_embedding = torch.stack([sample[i]['spk_embedding'] for i in order], dim=0)
        batch = {
            "utts": utts,
            "speech_token": speech_token,
            "speech_token_len": speech_token_len,
            "speech_feat": speech_feat,
            "speech_feat_len": speech_feat_len,
            "text": text,
            "text_token": text_token,
            "text_token_len": text_token_len,
            "utt_embedding": utt_embedding,
            "spk_embedding": spk_embedding,
        }
        if mode == 'inference':
            tts_text = [sample[i]['tts_text'] for i in order]
            tts_index = [sample[i]['tts_index'] for i in order]
            tts_text_token = [torch.tensor(sample[i]['tts_text_token']) for i in order]
            tts_text_token_len = torch.tensor([i.size(0) for i in tts_text_token], dtype=torch.int32)
            tts_text_token = pad_sequence(tts_text_token, batch_first=True, padding_value=-1)
            batch.update({'tts_text': tts_text,
                          'tts_index': tts_index,
                          'tts_text_token': tts_text_token,
                          'tts_text_token_len': tts_text_token_len})
        if use_spk_embedding is True:
            batch["embedding"] = batch["spk_embedding"]
        else:
            batch["embedding"] = batch["utt_embedding"]
        yield batch