funasr v2 setup

游雁

2023-11-21 c644ac8f58895b9e29e9cfca79465fd2c0efaa5a

funasr v2 setup

 egs/alimeeting/modular_sa_asr/local/make_textgrid_rttm.py

@@ -1,7 +1,10 @@
import argparse
import tqdm
import codecs
try:
import textgrid
except:
    raise "Please install textgrid firstly: pip install textgrid"
import pdb

class Segment(object):

 egs/alimeeting/modular_sa_asr/local/meeting_speaker_number_process.py

@@ -6,7 +6,10 @@
import codecs
from distutils.util import strtobool
from pathlib import Path
try:
import textgrid
except:
    raise "Please install textgrid firstly: pip install textgrid"
import pdb

class Segment(object):

 egs/alimeeting/sa_asr/local/alimeeting_process_overlap_force.py

@@ -6,7 +6,10 @@
import codecs
from distutils.util import strtobool
from pathlib import Path
try:
import textgrid
except:
    raise "Please install textgrid firstly: pip install textgrid"
import pdb

class Segment(object):

 egs/alimeeting/sa_asr/local/alimeeting_process_textgrid.py

@@ -6,7 +6,10 @@
import codecs
from distutils.util import strtobool
from pathlib import Path
try:
import textgrid
except:
    raise "Please install textgrid firstly: pip install textgrid"
import pdb

class Segment(object):

 egs/alimeeting/sa_asr/local/process_sot_fifo_textchar2spk.py

@@ -6,7 +6,10 @@
import codecs
from distutils.util import strtobool
from pathlib import Path
try:
import textgrid
except:
    raise "Please install textgrid firstly: pip install textgrid"
import pdb

def get_args():

 egs/alimeeting/sa_asr/local/process_textgrid_to_single_speaker_wav.py

@@ -6,7 +6,12 @@
import codecs
from distutils.util import strtobool
from pathlib import Path

try:
import textgrid
except:
    raise "Please install textgrid firstly: pip install textgrid"

import pdb
import numpy as np
import sys

 funasr/bin/asr_infer.py

@@ -44,9 +44,9 @@
    """Speech2Text class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2text(audio)
        [(text, token, token_int, hypothesis object), ...]

@@ -251,9 +251,9 @@
    """Speech2Text class

    Examples:
            >>> import soundfile
            >>> import librosa
            >>> speech2text = Speech2TextParaformer("asr_config.yml", "asr.pb")
            >>> audio, rate = soundfile.read("speech.wav")
            >>> audio, rate = librosa.load("speech.wav")
            >>> speech2text(audio)
            [(text, token, token_int, hypothesis object), ...]

@@ -625,9 +625,9 @@
    """Speech2Text class

    Examples:
            >>> import soundfile
            >>> import librosa
            >>> speech2text = Speech2TextParaformerOnline("asr_config.yml", "asr.pth")
            >>> audio, rate = soundfile.read("speech.wav")
            >>> audio, rate = librosa.load("speech.wav")
            >>> speech2text(audio)
            [(text, token, token_int, hypothesis object), ...]

@@ -876,9 +876,9 @@
    """Speech2Text class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> speech2text = Speech2TextUniASR("asr_config.yml", "asr.pb")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2text(audio)
        [(text, token, token_int, hypothesis object), ...]

@@ -1106,9 +1106,9 @@
    """Speech2Text class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> speech2text = Speech2TextMFCCA("asr_config.yml", "asr.pb")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2text(audio)
        [(text, token, token_int, hypothesis object), ...]

@@ -1637,9 +1637,9 @@
    """Speech2Text class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> speech2text = Speech2TextSAASR("asr_config.yml", "asr.pb")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2text(audio)
        [(text, token, token_int, hypothesis object), ...]

@@ -1885,9 +1885,9 @@
    """Speech2Text class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2text(audio)
        [(text, token, token_int, hypothesis object), ...]


 funasr/bin/asr_inference_launch.py

@@ -20,7 +20,8 @@
import numpy as np
import torch
import torchaudio
import soundfile
# import librosa
import librosa
import yaml

from funasr.bin.asr_infer import Speech2Text
@@ -1281,7 +1282,8 @@
            try:
                raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
            except:
                raw_inputs = soundfile.read(data_path_and_name_and_type[0], dtype='float32')[0]
                # raw_inputs = librosa.load(data_path_and_name_and_type[0], dtype='float32')[0]
                raw_inputs, sr = librosa.load(data_path_and_name_and_type[0], dtype='float32')
                if raw_inputs.ndim == 2:
                    raw_inputs = raw_inputs[:, 0]
                raw_inputs = torch.tensor(raw_inputs)

 funasr/bin/diar_infer.py

@@ -27,11 +27,11 @@
    """Speech2Diarlization class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> import numpy as np
        >>> speech2diar = Speech2DiarizationEEND("diar_sond_config.yml", "diar_sond.pb")
        >>> profile = np.load("profiles.npy")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2diar(audio, profile)
        {"spk1": [(int, int), ...], ...}

@@ -109,11 +109,11 @@
    """Speech2Xvector class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> import numpy as np
        >>> speech2diar = Speech2DiarizationSOND("diar_sond_config.yml", "diar_sond.pb")
        >>> profile = np.load("profiles.npy")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2diar(audio, profile)
        {"spk1": [(int, int), ...], ...}


 funasr/bin/diar_inference_launch.py

@@ -15,7 +15,8 @@
from typing import Union

import numpy as np
import soundfile
# import librosa
import librosa
import torch
from scipy.signal import medfilt

@@ -144,7 +145,9 @@
                        # read waveform file
                        example = [load_bytes(x) if isinstance(x, bytes) else x
                                   for x in example]
                        example = [soundfile.read(x)[0] if isinstance(x, str) else x
                        # example = [librosa.load(x)[0] if isinstance(x, str) else x
                        #            for x in example]
                        example = [librosa.load(x, dtype='float32')[0] if isinstance(x, str) else x
                                   for x in example]
                        # convert torch tensor to numpy array
                        example = [x.numpy() if isinstance(example[0], torch.Tensor) else x

 funasr/bin/ss_infer.py

@@ -20,9 +20,9 @@
    """SpeechSeparator class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> speech_separator = MossFormer("ss_config.yml", "ss.pt")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> separated_wavs = speech_separator(audio)        

    """

 funasr/bin/ss_inference_launch.py

@@ -13,7 +13,7 @@

import numpy as np
import torch
import soundfile as sf
import librosa
from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.utils import config_argparse
@@ -104,7 +104,12 @@
            ss_results = speech_separator(**batch)
            
            for spk in range(num_spks):
                sf.write(os.path.join(output_path, keys[0] + '_s' + str(spk+1)+'.wav'), ss_results[spk], sample_rate)
                # sf.write(os.path.join(output_path, keys[0] + '_s' + str(spk+1)+'.wav'), ss_results[spk], sample_rate)
                try:
                    librosa.output.write_wav(os.path.join(output_path, keys[0] + '_s' + str(spk+1)+'.wav'), ss_results[spk], sample_rate)
                except:
                    print("To write wav by librosa, you should install librosa<=0.8.0")
                    raise
        torch.cuda.empty_cache()
        return ss_results


 funasr/bin/sv_infer.py

@@ -22,9 +22,9 @@
    """Speech2Xvector class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> speech2xvector = Speech2Xvector("sv_config.yml", "sv.pb")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2xvector(audio)
        [(text, token, token_int, hypothesis object), ...]


 funasr/bin/vad_infer.py

@@ -23,9 +23,9 @@
    """Speech2VadSegment class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> speech2segment = Speech2VadSegment("vad_config.yml", "vad.pt")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2segment(audio)
        [[10, 230], [245, 450], ...]

@@ -118,9 +118,9 @@
    """Speech2VadSegmentOnline class

    Examples:
        >>> import soundfile
        >>> import librosa
        >>> speech2segment = Speech2VadSegmentOnline("vad_config.yml", "vad.pt")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> audio, rate = librosa.load("speech.wav")
        >>> speech2segment(audio)
        [[10, 230], [245, 450], ...]


 funasr/build_utils/build_trainer.py

@@ -246,14 +246,11 @@
        for iepoch in range(start_epoch, trainer_options.max_epoch + 1):
            if iepoch != start_epoch:
                logging.info(
                    "{}/{}epoch started. Estimated time to finish: {}".format(
                    "{}/{}epoch started. Estimated time to finish: {} hours".format(
                        iepoch,
                        trainer_options.max_epoch,
                        humanfriendly.format_timespan(
                            (time.perf_counter() - start_time)
                            / (iepoch - start_epoch)
                            * (trainer_options.max_epoch - iepoch + 1)
                        ),
                        (time.perf_counter() - start_time) / 3600.0 / (iepoch - start_epoch) * (
                                trainer_options.max_epoch - iepoch + 1),
                    )
                )
            else:

 funasr/datasets/dataset.py

@@ -16,8 +16,10 @@
from typing import Mapping
from typing import Tuple
from typing import Union

try:
import h5py
except:
    print("If you want use h5py dataset, please pip install h5py, and try it again")
import humanfriendly
import kaldiio
import numpy as np

 funasr/datasets/iterable_dataset.py

@@ -14,7 +14,8 @@
import numpy as np
import torch
import torchaudio
import soundfile
# import librosa
import librosa
from torch.utils.data.dataset import IterableDataset
import os.path

@@ -70,7 +71,8 @@
    try:
        return torchaudio.load(input)[0].numpy()
    except:
        waveform, _ = soundfile.read(input, dtype='float32')
        # waveform, _ = librosa.load(input, dtype='float32')
        waveform, _ = librosa.load(input, dtype='float32')
        if waveform.ndim == 2:
            waveform = waveform[:, 0]
        return np.expand_dims(waveform, axis=0)

 funasr/datasets/large_datasets/dataset.py

@@ -7,7 +7,8 @@
import torch.distributed as dist
import torchaudio
import numpy as np
import soundfile
# import librosa
import librosa
from kaldiio import ReadHelper
from torch.utils.data import IterableDataset

@@ -128,7 +129,8 @@
                        try:
                            waveform, sampling_rate = torchaudio.load(path)
                        except:
                            waveform, sampling_rate = soundfile.read(path, dtype='float32')
                            # waveform, sampling_rate = librosa.load(path, dtype='float32')
                            waveform, sampling_rate = librosa.load(path, dtype='float32')
                            if waveform.ndim == 2:
                                waveform = waveform[:, 0]
                            waveform = np.expand_dims(waveform, axis=0)

 funasr/datasets/preprocessor.py

@@ -10,7 +10,7 @@

import numpy as np
import scipy.signal
import soundfile
import librosa
import jieba

from funasr.text.build_tokenizer import build_tokenizer
@@ -284,7 +284,7 @@
                if self.rirs is not None and self.rir_apply_prob >= np.random.random():
                    rir_path = np.random.choice(self.rirs)
                    if rir_path is not None:
                        rir, _ = soundfile.read(
                        rir, _ = librosa.load(
                            rir_path, dtype=np.float64, always_2d=True
                        )

@@ -310,28 +310,31 @@
                        noise_db = np.random.uniform(
                            self.noise_db_low, self.noise_db_high
                        )
                        with soundfile.SoundFile(noise_path) as f:
                            if f.frames == nsamples:
                                noise = f.read(dtype=np.float64, always_2d=True)
                            elif f.frames < nsamples:
                                offset = np.random.randint(0, nsamples - f.frames)

                        audio_data = librosa.load(noise_path, dtype='float32')[0][None, :]
                        frames = len(audio_data[0])
                        if frames == nsamples:
                            noise = audio_data
                        elif frames < nsamples:
                            offset = np.random.randint(0, nsamples - frames)
                                # noise: (Time, Nmic)
                                noise = f.read(dtype=np.float64, always_2d=True)
                            noise = audio_data
                                # Repeat noise
                                noise = np.pad(
                                    noise,
                                    [(offset, nsamples - f.frames - offset), (0, 0)],
                                [(offset, nsamples - frames - offset), (0, 0)],
                                    mode="wrap",
                                )
                            else:
                                offset = np.random.randint(0, f.frames - nsamples)
                                f.seek(offset)
                            noise = audio_data[:, nsamples]
                            # offset = np.random.randint(0, frames - nsamples)
                            # f.seek(offset)
                                # noise: (Time, Nmic)
                                noise = f.read(
                                    nsamples, dtype=np.float64, always_2d=True
                                )
                                if len(noise) != nsamples:
                                    raise RuntimeError(f"Something wrong: {noise_path}")
                            # noise = f.read(
                            #     nsamples, dtype=np.float64, always_2d=True
                            # )
                            # if len(noise) != nsamples:
                            #     raise RuntimeError(f"Something wrong: {noise_path}")
                        # noise: (Nmic, Time)
                        noise = noise.T


 funasr/datasets/small_datasets/preprocessor.py

@@ -9,7 +9,7 @@

import numpy as np
import scipy.signal
import soundfile
import librosa

from funasr.text.build_tokenizer import build_tokenizer
from funasr.text.cleaner import TextCleaner
@@ -275,7 +275,7 @@
                if self.rirs is not None and self.rir_apply_prob >= np.random.random():
                    rir_path = np.random.choice(self.rirs)
                    if rir_path is not None:
                        rir, _ = soundfile.read(
                        rir, _ = librosa.load(
                            rir_path, dtype=np.float64, always_2d=True
                        )

@@ -301,28 +301,30 @@
                        noise_db = np.random.uniform(
                            self.noise_db_low, self.noise_db_high
                        )
                        with soundfile.SoundFile(noise_path) as f:
                            if f.frames == nsamples:
                                noise = f.read(dtype=np.float64, always_2d=True)
                            elif f.frames < nsamples:
                                offset = np.random.randint(0, nsamples - f.frames)
                        audio_data = librosa.load(noise_path, dtype='float32')[0][None, :]
                        frames = len(audio_data[0])
                        if frames == nsamples:
                            noise = audio_data
                        elif frames < nsamples:
                            offset = np.random.randint(0, nsamples - frames)
                                # noise: (Time, Nmic)
                                noise = f.read(dtype=np.float64, always_2d=True)
                            noise = audio_data
                                # Repeat noise
                                noise = np.pad(
                                    noise,
                                    [(offset, nsamples - f.frames - offset), (0, 0)],
                                [(offset, nsamples - frames - offset), (0, 0)],
                                    mode="wrap",
                                )
                            else:
                                offset = np.random.randint(0, f.frames - nsamples)
                                f.seek(offset)
                            noise = audio_data[:, nsamples]
                            # offset = np.random.randint(0, frames - nsamples)
                            # f.seek(offset)
                                # noise: (Time, Nmic)
                                noise = f.read(
                                    nsamples, dtype=np.float64, always_2d=True
                                )
                                if len(noise) != nsamples:
                                    raise RuntimeError(f"Something wrong: {noise_path}")
                            # noise = f.read(
                            #     nsamples, dtype=np.float64, always_2d=True
                            # )
                            # if len(noise) != nsamples:
                            #     raise RuntimeError(f"Something wrong: {noise_path}")
                        # noise: (Nmic, Time)
                        noise = noise.T


 funasr/export/export_conformer.py

File was deleted

 funasr/export/models/language_models/__init__.py

 funasr/export/models/language_models/embed.py

File was deleted

 funasr/export/models/language_models/seq_rnn.py

File was deleted

 funasr/export/models/language_models/subsampling.py

File was deleted

 funasr/export/models/language_models/transformer.py

File was deleted

 funasr/fileio/sound_scp.py

@@ -4,7 +4,7 @@

import random
import numpy as np
import soundfile
import librosa
import librosa

import torch
@@ -116,7 +116,7 @@
    def __getitem__(self, key):
        wav = self.data[key]
        if self.normalize:
            # soundfile.read normalizes data to [-1,1] if dtype is not given
            # librosa.load normalizes data to [-1,1] if dtype is not given
            array, rate = librosa.load(
                wav, sr=self.dest_sample_rate, mono=self.always_2d
            )

 funasr/layers/complex_utils.py

@@ -5,8 +5,12 @@
from typing import Union

import torch
try:
from torch_complex import functional as FC
from torch_complex.tensor import ComplexTensor
except:
    raise "Please install torch_complex firstly"



EPS = torch.finfo(torch.double).eps

 funasr/layers/stft.py

@@ -4,8 +4,11 @@
from typing import Union

import torch
from torch_complex.tensor import ComplexTensor

try:
    from torch_complex.tensor import ComplexTensor
except:
    raise "Please install torch_complex firstly"
from funasr.modules.nets_utils import make_pad_mask
from funasr.layers.complex_utils import is_complex
from funasr.layers.inversible_interface import InversibleInterface

 funasr/models/encoder/mossformer_encoder.py

@@ -1,8 +1,10 @@
import torch
import torch.nn as nn
import torch.nn.functional as F

try:
from rotary_embedding_torch import RotaryEmbedding
except:
    raise "Please install rotary_embedding_torch by: \n pip install -U funasr[all]"
from funasr.modules.layer_norm import GlobalLayerNorm, CumulativeLayerNorm, ScaleNorm
from funasr.modules.embedding import ScaledSinuEmbedding
from funasr.modules.mossformer import FLASH_ShareA_FFConvM

 funasr/models/frontend/default.py

@@ -6,7 +6,10 @@
import humanfriendly
import numpy as np
import torch
try:
from torch_complex.tensor import ComplexTensor
except:
    raise "Please install torch_complex firstly"

from funasr.layers.log_mel import LogMel
from funasr.layers.stft import Stft

 funasr/modules/eend_ola/utils/kaldi_data.py

@@ -9,7 +9,7 @@
import sys
import numpy as np
import subprocess
import soundfile as sf
import librosa as sf
import io
from functools import lru_cache

@@ -67,18 +67,18 @@
        # input piped command
        p = subprocess.Popen(wav_rxfilename[:-1], shell=True,
                             stdout=subprocess.PIPE)
        data, samplerate = sf.read(io.BytesIO(p.stdout.read()),
        data, samplerate = sf.load(io.BytesIO(p.stdout.read()),
                                   dtype='float32')
        # cannot seek
        data = data[start:end]
    elif wav_rxfilename == '-':
        # stdin
        data, samplerate = sf.read(sys.stdin, dtype='float32')
        data, samplerate = sf.load(sys.stdin, dtype='float32')
        # cannot seek
        data = data[start:end]
    else:
        # normal wav file
        data, samplerate = sf.read(wav_rxfilename, start=start, stop=end)
        data, samplerate = sf.load(wav_rxfilename, start=start, stop=end)
    return data, samplerate



 funasr/modules/frontends/__init__.py

File was deleted

 funasr/modules/frontends/beamformer.py

File was deleted

 funasr/modules/frontends/dnn_beamformer.py

File was deleted

 funasr/modules/frontends/dnn_wpe.py

File was deleted

 funasr/modules/frontends/feature_transform.py

File was deleted

 funasr/modules/frontends/frontend.py

File was deleted

 funasr/modules/frontends/mask_estimator.py

File was deleted

 funasr/train/trainer.py

@@ -278,14 +278,11 @@
        for iepoch in range(start_epoch, trainer_options.max_epoch + 1):
            if iepoch != start_epoch:
                logging.info(
                    "{}/{}epoch started. Estimated time to finish: {}".format(
                    "{}/{}epoch started. Estimated time to finish: {} hours".format(
                        iepoch,
                        trainer_options.max_epoch,
                        humanfriendly.format_timespan(
                            (time.perf_counter() - start_time)
                            / (iepoch - start_epoch)
                            * (trainer_options.max_epoch - iepoch + 1)
                        ),
                        (time.perf_counter() - start_time) / 3600.0 / (iepoch - start_epoch) * (
                                trainer_options.max_epoch - iepoch + 1),
                    )
                )
            else:

 funasr/utils/asr_utils.py

@@ -5,7 +5,7 @@
from typing import Any, Dict, List, Union

import torchaudio
import soundfile
import librosa
import numpy as np
import pkg_resources
from modelscope.utils.logger import get_logger
@@ -139,7 +139,7 @@
                    try:
                        audio, fs = torchaudio.load(fname)
                    except:
                        audio, fs = soundfile.read(fname)
                        audio, fs = librosa.load(fname)
                break
        if audio_type.rfind(".scp") >= 0:
            with open(fname, encoding="utf-8") as f:

 funasr/utils/prepare_data.py

@@ -5,7 +5,7 @@

import kaldiio
import numpy as np
import soundfile
import librosa
import torch.distributed as dist
import torchaudio

@@ -46,7 +46,7 @@
    try:
        waveform, sampling_rate = torchaudio.load(wav_path)
    except:
        waveform, sampling_rate = soundfile.read(wav_path)
        waveform, sampling_rate = librosa.load(wav_path)
        waveform = np.expand_dims(waveform, axis=0)
    n_frames = (waveform.shape[1] * 1000.0) / (sampling_rate * frontend_conf["frame_shift"] * frontend_conf["lfr_n"])
    feature_dim = frontend_conf["n_mels"] * frontend_conf["lfr_m"]

 funasr/utils/speaker_utils.py

@@ -12,7 +12,7 @@
from typing import Any, Dict, List, Union

import numpy as np
import soundfile as sf
import librosa as sf
import torch
import torchaudio
import logging
@@ -43,7 +43,7 @@
        for i in range(len(inputs)):
            if isinstance(inputs[i], str):
                file_bytes = File.read(inputs[i])
                data, fs = sf.read(io.BytesIO(file_bytes), dtype='float32')
                data, fs = sf.load(io.BytesIO(file_bytes), dtype='float32')
                if len(data.shape) == 2:
                    data = data[:, 0]
                data = torch.from_numpy(data).unsqueeze(0)

 funasr/utils/timestamp_tools.py

@@ -3,7 +3,7 @@
import logging
import argparse
import numpy as np
import edit_distance
# import edit_distance
from itertools import zip_longest


@@ -160,112 +160,112 @@
    return res


class AverageShiftCalculator():
    def __init__(self):
        logging.warning("Calculating average shift.")
    def __call__(self, file1, file2):
        uttid_list1, ts_dict1 = self.read_timestamps(file1)
        uttid_list2, ts_dict2 = self.read_timestamps(file2)
        uttid_intersection = self._intersection(uttid_list1, uttid_list2)
        res = self.as_cal(uttid_intersection, ts_dict1, ts_dict2)
        logging.warning("Average shift of {} and {}: {}.".format(file1, file2, str(res)[:8]))
        logging.warning("Following timestamp pair differs most: {}, detail:{}".format(self.max_shift, self.max_shift_uttid))

    def _intersection(self, list1, list2):
        set1 = set(list1)
        set2 = set(list2)
        if set1 == set2:
            logging.warning("Uttid same checked.")
            return set1
        itsc = list(set1 & set2)
        logging.warning("Uttid differs: file1 {}, file2 {}, lines same {}.".format(len(list1), len(list2), len(itsc)))
        return itsc

    def read_timestamps(self, file):
        # read timestamps file in standard format
        uttid_list = []
        ts_dict = {}
        with codecs.open(file, 'r') as fin:
            for line in fin.readlines():
                text = ''
                ts_list = []
                line = line.rstrip()
                uttid = line.split()[0]
                uttid_list.append(uttid)
                body = " ".join(line.split()[1:])
                for pd in body.split(';'):
                    if not len(pd): continue
                    # pdb.set_trace() 
                    char, start, end = pd.lstrip(" ").split(' ')
                    text += char + ','
                    ts_list.append((float(start), float(end)))
                # ts_lists.append(ts_list)
                ts_dict[uttid] = (text[:-1], ts_list)
        logging.warning("File {} read done.".format(file))
        return uttid_list, ts_dict

    def _shift(self, filtered_timestamp_list1, filtered_timestamp_list2):
        shift_time = 0
        for fts1, fts2 in zip(filtered_timestamp_list1, filtered_timestamp_list2):
            shift_time += abs(fts1[0] - fts2[0]) + abs(fts1[1] - fts2[1])
        num_tokens = len(filtered_timestamp_list1)
        return shift_time, num_tokens

    def as_cal(self, uttid_list, ts_dict1, ts_dict2):
        # calculate average shift between timestamp1 and timestamp2
        # when characters differ, use edit distance alignment
        # and calculate the error between the same characters
        self._accumlated_shift = 0
        self._accumlated_tokens = 0
        self.max_shift = 0
        self.max_shift_uttid = None
        for uttid in uttid_list:
            (t1, ts1) = ts_dict1[uttid]
            (t2, ts2) = ts_dict2[uttid]
            _align, _align2, _align3 = [], [], []
            fts1, fts2 = [], []
            _t1, _t2 = [], []
            sm = edit_distance.SequenceMatcher(t1.split(','), t2.split(','))
            s = sm.get_opcodes()
            for j in range(len(s)):
                if s[j][0] == "replace" or s[j][0] == "insert":
                    _align.append(0)
                if s[j][0] == "replace" or s[j][0] == "delete":
                    _align3.append(0)
                elif s[j][0] == "equal":
                    _align.append(1)
                    _align3.append(1)
                else:
                    continue
            # use s to index t2
            for a, ts , t in zip(_align, ts2, t2.split(',')):
                if a: 
                    fts2.append(ts)
                    _t2.append(t)
            sm2 = edit_distance.SequenceMatcher(t2.split(','), t1.split(','))
            s = sm2.get_opcodes()
            for j in range(len(s)):
                if s[j][0] == "replace" or s[j][0] == "insert":
                    _align2.append(0)
                elif s[j][0] == "equal":
                    _align2.append(1)
                else:
                    continue
            # use s2 tp index t1
            for a, ts, t in zip(_align3, ts1, t1.split(',')):
                if a: 
                    fts1.append(ts)
                    _t1.append(t)
            if len(fts1) == len(fts2):
                shift_time, num_tokens = self._shift(fts1, fts2)
                self._accumlated_shift += shift_time
                self._accumlated_tokens += num_tokens
                if shift_time/num_tokens > self.max_shift:
                    self.max_shift = shift_time/num_tokens
                    self.max_shift_uttid = uttid
            else:
                logging.warning("length mismatch")
        return self._accumlated_shift / self._accumlated_tokens
# class AverageShiftCalculator():
#     def __init__(self):
#         logging.warning("Calculating average shift.")
#     def __call__(self, file1, file2):
#         uttid_list1, ts_dict1 = self.read_timestamps(file1)
#         uttid_list2, ts_dict2 = self.read_timestamps(file2)
#         uttid_intersection = self._intersection(uttid_list1, uttid_list2)
#         res = self.as_cal(uttid_intersection, ts_dict1, ts_dict2)
#         logging.warning("Average shift of {} and {}: {}.".format(file1, file2, str(res)[:8]))
#         logging.warning("Following timestamp pair differs most: {}, detail:{}".format(self.max_shift, self.max_shift_uttid))
#
#     def _intersection(self, list1, list2):
#         set1 = set(list1)
#         set2 = set(list2)
#         if set1 == set2:
#             logging.warning("Uttid same checked.")
#             return set1
#         itsc = list(set1 & set2)
#         logging.warning("Uttid differs: file1 {}, file2 {}, lines same {}.".format(len(list1), len(list2), len(itsc)))
#         return itsc
#
#     def read_timestamps(self, file):
#         # read timestamps file in standard format
#         uttid_list = []
#         ts_dict = {}
#         with codecs.open(file, 'r') as fin:
#             for line in fin.readlines():
#                 text = ''
#                 ts_list = []
#                 line = line.rstrip()
#                 uttid = line.split()[0]
#                 uttid_list.append(uttid)
#                 body = " ".join(line.split()[1:])
#                 for pd in body.split(';'):
#                     if not len(pd): continue
#                     # pdb.set_trace()
#                     char, start, end = pd.lstrip(" ").split(' ')
#                     text += char + ','
#                     ts_list.append((float(start), float(end)))
#                 # ts_lists.append(ts_list)
#                 ts_dict[uttid] = (text[:-1], ts_list)
#         logging.warning("File {} read done.".format(file))
#         return uttid_list, ts_dict
#
#     def _shift(self, filtered_timestamp_list1, filtered_timestamp_list2):
#         shift_time = 0
#         for fts1, fts2 in zip(filtered_timestamp_list1, filtered_timestamp_list2):
#             shift_time += abs(fts1[0] - fts2[0]) + abs(fts1[1] - fts2[1])
#         num_tokens = len(filtered_timestamp_list1)
#         return shift_time, num_tokens
#
#     # def as_cal(self, uttid_list, ts_dict1, ts_dict2):
#     #     # calculate average shift between timestamp1 and timestamp2
#     #     # when characters differ, use edit distance alignment
#     #     # and calculate the error between the same characters
#     #     self._accumlated_shift = 0
#     #     self._accumlated_tokens = 0
#     #     self.max_shift = 0
#     #     self.max_shift_uttid = None
#     #     for uttid in uttid_list:
#     #         (t1, ts1) = ts_dict1[uttid]
#     #         (t2, ts2) = ts_dict2[uttid]
#     #         _align, _align2, _align3 = [], [], []
#     #         fts1, fts2 = [], []
#     #         _t1, _t2 = [], []
#     #         sm = edit_distance.SequenceMatcher(t1.split(','), t2.split(','))
#     #         s = sm.get_opcodes()
#     #         for j in range(len(s)):
#     #             if s[j][0] == "replace" or s[j][0] == "insert":
#     #                 _align.append(0)
#     #             if s[j][0] == "replace" or s[j][0] == "delete":
#     #                 _align3.append(0)
#     #             elif s[j][0] == "equal":
#     #                 _align.append(1)
#     #                 _align3.append(1)
#     #             else:
#     #                 continue
#     #         # use s to index t2
#     #         for a, ts , t in zip(_align, ts2, t2.split(',')):
#     #             if a:
#     #                 fts2.append(ts)
#     #                 _t2.append(t)
#     #         sm2 = edit_distance.SequenceMatcher(t2.split(','), t1.split(','))
#     #         s = sm2.get_opcodes()
#     #         for j in range(len(s)):
#     #             if s[j][0] == "replace" or s[j][0] == "insert":
#     #                 _align2.append(0)
#     #             elif s[j][0] == "equal":
#     #                 _align2.append(1)
#     #             else:
#     #                 continue
#     #         # use s2 tp index t1
#     #         for a, ts, t in zip(_align3, ts1, t1.split(',')):
#     #             if a:
#     #                 fts1.append(ts)
#     #                 _t1.append(t)
#     #         if len(fts1) == len(fts2):
#     #             shift_time, num_tokens = self._shift(fts1, fts2)
#     #             self._accumlated_shift += shift_time
#     #             self._accumlated_tokens += num_tokens
#     #             if shift_time/num_tokens > self.max_shift:
#     #                 self.max_shift = shift_time/num_tokens
#     #                 self.max_shift_uttid = uttid
#     #         else:
#     #             logging.warning("length mismatch")
#     #     return self._accumlated_shift / self._accumlated_tokens


def convert_external_alphas(alphas_file, text_file, output_file):
@@ -311,10 +311,10 @@


def main(args):
    if args.mode == 'cal_aas':
        asc = AverageShiftCalculator()
        asc(args.input, args.input2)
    elif args.mode == 'read_ext_alphas':
    # if args.mode == 'cal_aas':
    #     asc = AverageShiftCalculator()
    #     asc(args.input, args.input2)
    if args.mode == 'read_ext_alphas':
        convert_external_alphas(args.input, args.input2, args.output)
    else:
        logging.error("Mode {} not in SUPPORTED_MODES: {}.".format(args.mode, SUPPORTED_MODES))

 funasr/utils/wav_utils.py

@@ -11,7 +11,7 @@
import numpy as np
import torch
import torchaudio
import soundfile
import librosa
import torchaudio.compliance.kaldi as kaldi


@@ -166,7 +166,7 @@
        try:
            waveform, audio_sr = torchaudio.load(wav_file)
        except:
            waveform, audio_sr = soundfile.read(wav_file, dtype='float32')
            waveform, audio_sr = librosa.load(wav_file, dtype='float32')
            if waveform.ndim == 2:
                waveform = waveform[:, 0]
            waveform = torch.tensor(np.expand_dims(waveform, axis=0))
@@ -191,7 +191,7 @@
    try:
        waveform, sampling_rate = torchaudio.load(wav_path)
    except:
        waveform, sampling_rate = soundfile.read(wav_path)
        waveform, sampling_rate = librosa.load(wav_path)
        waveform = torch.tensor(np.expand_dims(waveform, axis=0))
    speech_length = (waveform.shape[1] / sampling_rate) * 1000.
    n_frames = (waveform.shape[1] * 1000.0) / (sampling_rate * frontend_conf["frame_shift"] * frontend_conf["lfr_n"])

 funasr/utils/whisper_utils/audio.py

@@ -1,8 +1,11 @@
import os
from functools import lru_cache
from typing import Union

try:
import ffmpeg
except:
    print("Please Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.")

import numpy as np
import torch
import torch.nn.functional as F

 setup.py

@@ -10,36 +10,36 @@

requirements = {
    "install": [
        "setuptools>=38.5.1",
        # "setuptools>=38.5.1",
        "humanfriendly",
        "scipy>=1.4.1",
        "librosa",
        "jamo",  # For kss
        # "jamo",  # For kss
        "PyYAML>=5.1.2",
        "soundfile>=0.12.1",
        "h5py>=3.1.0",
        # "soundfile>=0.12.1",
        # "h5py>=3.1.0",
        "kaldiio>=2.17.0",
        "torch_complex",
        "nltk>=3.4.5",
        # "torch_complex",
        # "nltk>=3.4.5",
        # ASR
        "sentencepiece",
        "sentencepiece", # train
        "jieba",
        "rotary_embedding_torch",
        "ffmpeg",
        # "rotary_embedding_torch",
        # "ffmpeg-python",
        # TTS
        "pypinyin>=0.44.0",
        "espnet_tts_frontend",
        # "pypinyin>=0.44.0",
        # "espnet_tts_frontend",
        # ENH
        "pytorch_wpe",
        # "pytorch_wpe",
        "editdistance>=0.5.2",
        "tensorboard",
        "g2p",
        "nara_wpe",
        # "g2p",
        # "nara_wpe",
        # PAI
        "oss2",
        "edit-distance",
        "textgrid",
        "protobuf",
        # "edit-distance",
        # "textgrid",
        # "protobuf",
        "tqdm",
        "hdbscan",
        "umap",
@@ -104,7 +104,7 @@
    name="funasr",
    version=version,
    url="https://github.com/alibaba-damo-academy/FunASR.git",
    author="Speech Lab of DAMO Academy, Alibaba Group",
    author="Speech Lab of Alibaba Group",
    author_email="funasr@list.alibaba-inc.com",
    description="FunASR: A Fundamental End-to-End Speech Recognition Toolkit",
    long_description=open(os.path.join(dirname, "README.md"), encoding="utf-8").read(),