funasr v2 setup

egs/alimeeting/modular_sa_asr/local/make_textgrid_rttm.py

@@ -1,7 +1,10 @@
 import argparse
 import tqdm
 import codecs
-import textgrid
+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 argparse
 import codecs
 from distutils.util import strtobool
 from pathlib import Path
-import textgrid
+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 argparse
 import codecs
 from distutils.util import strtobool
 from pathlib import Path
-import textgrid
+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 argparse
 import codecs
 from distutils.util import strtobool
 from pathlib import Path
-import textgrid
+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 argparse
 import codecs
 from distutils.util import strtobool
 from pathlib import Path
-import textgrid
+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 argparse
 import codecs
 from distutils.util import strtobool
 from pathlib import Path
-import textgrid
+
+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 @@ class Speech2Text:
     """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 @@ class Speech2TextParaformer:
     """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 @@ class Speech2TextParaformerOnline:
     """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 @@ class Speech2TextUniASR:
     """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 @@ class Speech2TextMFCCA:
     """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 @@ class Speech2TextSAASR:
     """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 @@ class Speech2TextWhisper:
     """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 @@ from typing import Union
 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 @@ def inference_paraformer_online(
             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 @@ class Speech2DiarizationEEND:
     """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 @@ class Speech2DiarizationSOND:
     """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 Tuple
 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 @@ def inference_sond(
                         # 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 @@ class SpeechSeparator:
     """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 @@ from typing import Union
 
 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 @@ def inference_ss(
             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 @@ class Speech2Xvector:
     """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 @@ class Speech2VadSegment:
     """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 @@ class Speech2VadSegmentOnline(Speech2VadSegment):
     """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 @@ class Trainer:
         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 Dict
 from typing import Mapping
 from typing import Tuple
 from typing import Union
-
-import h5py
+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 kaldiio
 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 @@ def load_wav(input):
     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
 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 @@ class AudioDataset(IterableDataset):
                         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 @@ from typing import Union
 
 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 @@ class CommonPreprocessor(AbsPreprocessor):
                 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 @@ class CommonPreprocessor(AbsPreprocessor):
                         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)
-                                # noise: (Time, Nmic)
-                                noise = f.read(dtype=np.float64, always_2d=True)
-                                # Repeat noise
-                                noise = np.pad(
-                                    noise,
-                                    [(offset, nsamples - f.frames - offset), (0, 0)],
-                                    mode="wrap",
-                                )
-                            else:
-                                offset = np.random.randint(0, f.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}")
+
+                        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 = audio_data
+                            # Repeat noise
+                            noise = np.pad(
+                                noise,
+                                [(offset, nsamples - frames - offset), (0, 0)],
+                                mode="wrap",
+                            )
+                        else:
+                            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: (Nmic, Time)
                         noise = noise.T
 

funasr/datasets/small_datasets/preprocessor.py

@@ -9,7 +9,7 @@ from typing import Union
 
 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 @@ class CommonPreprocessor(AbsPreprocessor):
                 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 @@ class CommonPreprocessor(AbsPreprocessor):
                         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)
-                                # noise: (Time, Nmic)
-                                noise = f.read(dtype=np.float64, always_2d=True)
-                                # Repeat noise
-                                noise = np.pad(
-                                    noise,
-                                    [(offset, nsamples - f.frames - offset), (0, 0)],
-                                    mode="wrap",
-                                )
-                            else:
-                                offset = np.random.randint(0, f.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}")
+                        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 = audio_data
+                            # Repeat noise
+                            noise = np.pad(
+                                noise,
+                                [(offset, nsamples - frames - offset), (0, 0)],
+                                mode="wrap",
+                            )
+                        else:
+                            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: (Nmic, Time)
                         noise = noise.T
 

funasr/export/export_conformer.py

@@ -1,151 +0,0 @@
-import json
-from typing import Union, Dict
-from pathlib import Path
-
-import os
-import logging
-import torch
-
-from funasr.export.models import get_model
-import numpy as np
-import random
-from funasr.utils.types import str2bool, str2triple_str
-# torch_version = float(".".join(torch.__version__.split(".")[:2]))
-# assert torch_version > 1.9
-
-class ModelExport:
-    def __init__(
-        self,
-        cache_dir: Union[Path, str] = None,
-        onnx: bool = True,
-        device: str = "cpu",
-        quant: bool = True,
-        fallback_num: int = 0,
-        audio_in: str = None,
-        calib_num: int = 200,
-        model_revision: str = None,
-    ):
-        self.set_all_random_seed(0)
-
-        self.cache_dir = cache_dir
-        self.export_config = dict(
-            feats_dim=560,
-            onnx=False,
-        )
-        
-        self.onnx = onnx
-        self.device = device
-        self.quant = quant
-        self.fallback_num = fallback_num
-        self.frontend = None
-        self.audio_in = audio_in
-        self.calib_num = calib_num
-        self.model_revision = model_revision
-
-    def _export(
-        self,
-        model,
-        model_dir: str = None,
-        verbose: bool = False,
-    ):
-
-        export_dir = model_dir
-        os.makedirs(export_dir, exist_ok=True)
-
-        self.export_config["model_name"] = "model"
-        model = get_model(
-            model,
-            self.export_config,
-        )
-        model.eval()
-
-        if self.onnx:
-            self._export_onnx(model, verbose, export_dir)
-
-        print("output dir: {}".format(export_dir))
-
-    def _export_onnx(self, model, verbose, path):
-        model._export_onnx(verbose, path)
-
-    def set_all_random_seed(self, seed: int):
-        random.seed(seed)
-        np.random.seed(seed)
-        torch.random.manual_seed(seed)
-
-    def parse_audio_in(self, audio_in):
-        
-        wav_list, name_list = [], []
-        if audio_in.endswith(".scp"):
-            f = open(audio_in, 'r')
-            lines = f.readlines()[:self.calib_num]
-            for line in lines:
-                name, path = line.strip().split()
-                name_list.append(name)
-                wav_list.append(path)
-        else:
-            wav_list = [audio_in,]
-            name_list = ["test",]
-        return wav_list, name_list
-    
-    def load_feats(self, audio_in: str = None):
-        import torchaudio
-
-        wav_list, name_list = self.parse_audio_in(audio_in)
-        feats = []
-        feats_len = []
-        for line in wav_list:
-            path = line.strip()
-            waveform, sampling_rate = torchaudio.load(path)
-            if sampling_rate != self.frontend.fs:
-                waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate,
-                                                          new_freq=self.frontend.fs)(waveform)
-            fbank, fbank_len = self.frontend(waveform, [waveform.size(1)])
-            feats.append(fbank)
-            feats_len.append(fbank_len)
-        return feats, feats_len
-
-    def export(self,
-               mode: str = None,
-               ):
-
-        if mode.startswith('conformer'):
-            from funasr.tasks.asr import ASRTask
-            config = os.path.join(model_dir, 'config.yaml')
-            model_file = os.path.join(model_dir, 'model.pb')
-            cmvn_file = os.path.join(model_dir, 'am.mvn')
-            model, asr_train_args = ASRTask.build_model_from_file(
-                config, model_file, cmvn_file, 'cpu'
-            )
-            self.frontend = model.frontend
-            self.export_config["feats_dim"] = 560
-
-        self._export(model, self.cache_dir)
-
-if __name__ == '__main__':
-    import argparse
-    parser = argparse.ArgumentParser()
-    # parser.add_argument('--model-name', type=str, required=True)
-    parser.add_argument('--model-name', type=str, action="append", required=True, default=[])
-    parser.add_argument('--export-dir', type=str, required=True)
-    parser.add_argument('--type', type=str, default='onnx', help='["onnx", "torch"]')
-    parser.add_argument('--device', type=str, default='cpu', help='["cpu", "cuda"]')
-    parser.add_argument('--quantize', type=str2bool, default=False, help='export quantized model')
-    parser.add_argument('--fallback-num', type=int, default=0, help='amp fallback number')
-    parser.add_argument('--audio_in', type=str, default=None, help='["wav", "wav.scp"]')
-    parser.add_argument('--calib_num', type=int, default=200, help='calib max num')
-    parser.add_argument('--model_revision', type=str, default=None, help='model_revision')
-    args = parser.parse_args()
-
-    export_model = ModelExport(
-        cache_dir=args.export_dir,
-        onnx=args.type == 'onnx',
-        device=args.device,
-        quant=args.quantize,
-        fallback_num=args.fallback_num,
-        audio_in=args.audio_in,
-        calib_num=args.calib_num,
-        model_revision=args.model_revision,
-    )
-    for model_name in args.model_name:
-        print("export model: {}".format(model_name))
-        export_model.export(model_name)

funasr/export/models/language_models/embed.py

@@ -1,403 +0,0 @@
-"""Positional Encoding Module."""
-
-import math
-
-import torch
-import torch.nn as nn
-from funasr.modules.embedding import (
-    LegacyRelPositionalEncoding, PositionalEncoding, RelPositionalEncoding,
-    ScaledPositionalEncoding, StreamPositionalEncoding)
-from funasr.modules.subsampling import (
-    Conv2dSubsampling, Conv2dSubsampling2, Conv2dSubsampling6,
-    Conv2dSubsampling8)
-from funasr.modules.subsampling_without_posenc import \
-    Conv2dSubsamplingWOPosEnc
-
-from funasr.export.models.language_models.subsampling import (
-    OnnxConv2dSubsampling, OnnxConv2dSubsampling2, OnnxConv2dSubsampling6,
-    OnnxConv2dSubsampling8)
-
-
-def get_pos_emb(pos_emb, max_seq_len=512, use_cache=True):
-    if isinstance(pos_emb, LegacyRelPositionalEncoding):
-        return OnnxLegacyRelPositionalEncoding(pos_emb, max_seq_len, use_cache)
-    elif isinstance(pos_emb, ScaledPositionalEncoding):
-        return OnnxScaledPositionalEncoding(pos_emb, max_seq_len, use_cache)
-    elif isinstance(pos_emb, RelPositionalEncoding):
-        return OnnxRelPositionalEncoding(pos_emb, max_seq_len, use_cache)
-    elif isinstance(pos_emb, PositionalEncoding):
-        return OnnxPositionalEncoding(pos_emb, max_seq_len, use_cache)
-    elif isinstance(pos_emb, StreamPositionalEncoding):
-        return OnnxStreamPositionalEncoding(pos_emb, max_seq_len, use_cache)
-    elif (isinstance(pos_emb, nn.Sequential) and len(pos_emb) == 0) or (
-        isinstance(pos_emb, Conv2dSubsamplingWOPosEnc)
-    ):
-        return pos_emb
-    else:
-        raise ValueError("Embedding model is not supported.")
-
-
-class Embedding(nn.Module):
-    def __init__(self, model, max_seq_len=512, use_cache=True):
-        super().__init__()
-        self.model = model
-        if not isinstance(model, nn.Embedding):
-            if isinstance(model, Conv2dSubsampling):
-                self.model = OnnxConv2dSubsampling(model)
-                self.model.out[-1] = get_pos_emb(model.out[-1], max_seq_len)
-            elif isinstance(model, Conv2dSubsampling2):
-                self.model = OnnxConv2dSubsampling2(model)
-                self.model.out[-1] = get_pos_emb(model.out[-1], max_seq_len)
-            elif isinstance(model, Conv2dSubsampling6):
-                self.model = OnnxConv2dSubsampling6(model)
-                self.model.out[-1] = get_pos_emb(model.out[-1], max_seq_len)
-            elif isinstance(model, Conv2dSubsampling8):
-                self.model = OnnxConv2dSubsampling8(model)
-                self.model.out[-1] = get_pos_emb(model.out[-1], max_seq_len)
-            else:
-                self.model[-1] = get_pos_emb(model[-1], max_seq_len)
-
-    def forward(self, x, mask=None):
-        if mask is None:
-            return self.model(x)
-        else:
-            return self.model(x, mask)
-
-
-def _pre_hook(
-    state_dict,
-    prefix,
-    local_metadata,
-    strict,
-    missing_keys,
-    unexpected_keys,
-    error_msgs,
-):
-    """Perform pre-hook in load_state_dict for backward compatibility.
-
-    Note:
-        We saved self.pe until v.0.5.2 but we have omitted it later.
-        Therefore, we remove the item "pe" from `state_dict` for backward compatibility.
-
-    """
-    k = prefix + "pe"
-    if k in state_dict:
-        state_dict.pop(k)
-
-
-class OnnxPositionalEncoding(torch.nn.Module):
-    """Positional encoding.
-
-    Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_seq_len (int): Maximum input length.
-        reverse (bool): Whether to reverse the input position. Only for
-        the class LegacyRelPositionalEncoding. We remove it in the current
-        class RelPositionalEncoding.
-    """
-
-    def __init__(self, model, max_seq_len=512, reverse=False, use_cache=True):
-        """Construct an PositionalEncoding object."""
-        super(OnnxPositionalEncoding, self).__init__()
-        self.d_model = model.d_model
-        self.reverse = reverse
-        self.max_seq_len = max_seq_len
-        self.xscale = math.sqrt(self.d_model)
-        self._register_load_state_dict_pre_hook(_pre_hook)
-        self.pe = model.pe
-        self.use_cache = use_cache
-        self.model = model
-        if self.use_cache:
-            self.extend_pe()
-        else:
-            self.div_term = torch.exp(
-                torch.arange(0, self.d_model, 2, dtype=torch.float32)
-                * -(math.log(10000.0) / self.d_model)
-            )
-
-    def extend_pe(self):
-        """Reset the positional encodings."""
-        pe_length = len(self.pe[0])
-        if self.max_seq_len < pe_length:
-            self.pe = self.pe[:, : self.max_seq_len]
-        else:
-            self.model.extend_pe(torch.tensor(0.0).expand(1, self.max_seq_len))
-            self.pe = self.model.pe
-
-    def _add_pe(self, x):
-        """Computes positional encoding"""
-        if self.reverse:
-            position = torch.arange(
-                x.size(1) - 1, -1, -1.0, dtype=torch.float32
-            ).unsqueeze(1)
-        else:
-            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
-
-        x = x * self.xscale
-        x[:, :, 0::2] += torch.sin(position * self.div_term)
-        x[:, :, 1::2] += torch.cos(position * self.div_term)
-        return x
-
-    def forward(self, x: torch.Tensor):
-        """Add positional encoding.
-
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, `*`).
-
-        Returns:
-            torch.Tensor: Encoded tensor (batch, time, `*`).
-        """
-        if self.use_cache:
-            x = x * self.xscale + self.pe[:, : x.size(1)]
-        else:
-            x = self._add_pe(x)
-        return x
-
-
-class OnnxScaledPositionalEncoding(OnnxPositionalEncoding):
-    """Scaled positional encoding module.
-
-    See Sec. 3.2  https://arxiv.org/abs/1809.08895
-
-    Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_seq_len (int): Maximum input length.
-
-    """
-
-    def __init__(self, model, max_seq_len=512, use_cache=True):
-        """Initialize class."""
-        super().__init__(model, max_seq_len, use_cache=use_cache)
-        self.alpha = torch.nn.Parameter(torch.tensor(1.0))
-
-    def reset_parameters(self):
-        """Reset parameters."""
-        self.alpha.data = torch.tensor(1.0)
-
-    def _add_pe(self, x):
-        """Computes positional encoding"""
-        if self.reverse:
-            position = torch.arange(
-                x.size(1) - 1, -1, -1.0, dtype=torch.float32
-            ).unsqueeze(1)
-        else:
-            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
-
-        x = x * self.alpha
-        x[:, :, 0::2] += torch.sin(position * self.div_term)
-        x[:, :, 1::2] += torch.cos(position * self.div_term)
-        return x
-
-    def forward(self, x):
-        """Add positional encoding.
-
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, `*`).
-
-        Returns:
-            torch.Tensor: Encoded tensor (batch, time, `*`).
-
-        """
-        if self.use_cache:
-            x = x + self.alpha * self.pe[:, : x.size(1)]
-        else:
-            x = self._add_pe(x)
-        return x
-
-
-class OnnxLegacyRelPositionalEncoding(OnnxPositionalEncoding):
-    """Relative positional encoding module (old version).
-
-    Details can be found in https://github.com/espnet/espnet/pull/2816.
-
-    See : Appendix B in https://arxiv.org/abs/1901.02860
-
-    Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_seq_len (int): Maximum input length.
-
-    """
-
-    def __init__(self, model, max_seq_len=512, use_cache=True):
-        """Initialize class."""
-        super().__init__(model, max_seq_len, reverse=True, use_cache=use_cache)
-
-    def _get_pe(self, x):
-        """Computes positional encoding"""
-        if self.reverse:
-            position = torch.arange(
-                x.size(1) - 1, -1, -1.0, dtype=torch.float32
-            ).unsqueeze(1)
-        else:
-            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
-
-        pe = torch.zeros(x.shape)
-        pe[:, :, 0::2] += torch.sin(position * self.div_term)
-        pe[:, :, 1::2] += torch.cos(position * self.div_term)
-        return pe
-
-    def forward(self, x):
-        """Compute positional encoding.
-
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, `*`).
-
-        Returns:
-            torch.Tensor: Encoded tensor (batch, time, `*`).
-            torch.Tensor: Positional embedding tensor (1, time, `*`).
-
-        """
-        x = x * self.xscale
-        if self.use_cache:
-            pos_emb = self.pe[:, : x.size(1)]
-        else:
-            pos_emb = self._get_pe(x)
-        return x, pos_emb
-
-
-class OnnxRelPositionalEncoding(torch.nn.Module):
-    """Relative positional encoding module (new implementation).
-    Details can be found in https://github.com/espnet/espnet/pull/2816.
-    See : Appendix B in https://arxiv.org/abs/1901.02860
-    Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_seq_len (int): Maximum input length.
-    """
-
-    def __init__(self, model, max_seq_len=512, use_cache=True):
-        """Construct an PositionalEncoding object."""
-        super(OnnxRelPositionalEncoding, self).__init__()
-        self.d_model = model.d_model
-        self.xscale = math.sqrt(self.d_model)
-        self.pe = None
-        self.use_cache = use_cache
-        if self.use_cache:
-            self.extend_pe(torch.tensor(0.0).expand(1, max_seq_len))
-        else:
-            self.div_term = torch.exp(
-                torch.arange(0, self.d_model, 2, dtype=torch.float32)
-                * -(math.log(10000.0) / self.d_model)
-            )
-
-    def extend_pe(self, x):
-        """Reset the positional encodings."""
-        if self.pe is not None and self.pe.size(1) >= x.size(1) * 2 - 1:
-            # self.pe contains both positive and negative parts
-            # the length of self.pe is 2 * input_len - 1
-            if self.pe.dtype != x.dtype or self.pe.device != x.device:
-                self.pe = self.pe.to(dtype=x.dtype, device=x.device)
-            return
-        # Suppose `i` means to the position of query vecotr and `j` means the
-        # position of key vector. We use position relative positions when keys
-        # are to the left (i>j) and negative relative positions otherwise (i<j).
-        pe_positive = torch.zeros(x.size(1), self.d_model)
-        pe_negative = torch.zeros(x.size(1), self.d_model)
-        position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
-        div_term = torch.exp(
-            torch.arange(0, self.d_model, 2, dtype=torch.float32)
-            * -(math.log(10000.0) / self.d_model)
-        )
-        pe_positive[:, 0::2] = torch.sin(position * div_term)
-        pe_positive[:, 1::2] = torch.cos(position * div_term)
-        pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
-        pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
-
-        # Reserve the order of positive indices and concat both positive and
-        # negative indices. This is used to support the shifting trick
-        # as in https://arxiv.org/abs/1901.02860
-        pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
-        pe_negative = pe_negative[1:].unsqueeze(0)
-        pe = torch.cat([pe_positive, pe_negative], dim=1)
-        self.pe = pe.to(device=x.device, dtype=x.dtype)
-
-    def _get_pe(self, x):
-        pe_positive = torch.zeros(x.size(1), self.d_model)
-        pe_negative = torch.zeros(x.size(1), self.d_model)
-        theta = (
-            torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) * self.div_term
-        )
-        pe_positive[:, 0::2] = torch.sin(theta)
-        pe_positive[:, 1::2] = torch.cos(theta)
-        pe_negative[:, 0::2] = -1 * torch.sin(theta)
-        pe_negative[:, 1::2] = torch.cos(theta)
-
-        # Reserve the order of positive indices and concat both positive and
-        # negative indices. This is used to support the shifting trick
-        # as in https://arxiv.org/abs/1901.02860
-        pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
-        pe_negative = pe_negative[1:].unsqueeze(0)
-        return torch.cat([pe_positive, pe_negative], dim=1)
-
-    def forward(self, x: torch.Tensor, use_cache=True):
-        """Add positional encoding.
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, `*`).
-        Returns:
-            torch.Tensor: Encoded tensor (batch, time, `*`).
-        """
-        x = x * self.xscale
-        if self.use_cache:
-            pos_emb = self.pe[
-                :,
-                self.pe.size(1) // 2 - x.size(1) + 1 : self.pe.size(1) // 2 + x.size(1),
-            ]
-        else:
-            pos_emb = self._get_pe(x)
-        return x, pos_emb
-
-
-class OnnxStreamPositionalEncoding(torch.nn.Module):
-    """Streaming Positional encoding."""
-
-    def __init__(self, model, max_seq_len=5000, use_cache=True):
-        """Construct an PositionalEncoding object."""
-        super(StreamPositionalEncoding, self).__init__()
-        self.use_cache = use_cache
-        self.d_model = model.d_model
-        self.xscale = model.xscale
-        self.pe = model.pe
-        self.use_cache = use_cache
-        self.max_seq_len = max_seq_len
-        if self.use_cache:
-            self.extend_pe()
-        else:
-            self.div_term = torch.exp(
-                torch.arange(0, self.d_model, 2, dtype=torch.float32)
-                * -(math.log(10000.0) / self.d_model)
-            )
-        self._register_load_state_dict_pre_hook(_pre_hook)
-
-    def extend_pe(self):
-        """Reset the positional encodings."""
-        pe_length = len(self.pe[0])
-        if self.max_seq_len < pe_length:
-            self.pe = self.pe[:, : self.max_seq_len]
-        else:
-            self.model.extend_pe(self.max_seq_len)
-            self.pe = self.model.pe
-
-    def _add_pe(self, x, start_idx):
-        position = torch.arange(start_idx, x.size(1), dtype=torch.float32).unsqueeze(1)
-        x = x * self.xscale
-        x[:, :, 0::2] += torch.sin(position * self.div_term)
-        x[:, :, 1::2] += torch.cos(position * self.div_term)
-        return x
-
-    def forward(self, x: torch.Tensor, start_idx: int = 0):
-        """Add positional encoding.
-
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, `*`).
-
-        Returns:
-            torch.Tensor: Encoded tensor (batch, time, `*`).
-
-        """
-        if self.use_cache:
-            return x * self.xscale + self.pe[:, start_idx : start_idx + x.size(1)]
-        else:
-            return self._add_pe(x, start_idx)

funasr/export/models/language_models/seq_rnn.py

@@ -1,84 +0,0 @@
-import os
-
-import torch
-import torch.nn as nn
-
-class SequentialRNNLM(nn.Module):
-    def __init__(self, model, **kwargs):
-        super().__init__()
-        self.encoder = model.encoder
-        self.rnn = model.rnn
-        self.rnn_type = model.rnn_type
-        self.decoder = model.decoder
-        self.nlayers = model.nlayers
-        self.nhid = model.nhid
-        self.model_name = "seq_rnnlm"
-
-    def forward(self, y, hidden1, hidden2=None):
-        # batch_score function.
-        emb = self.encoder(y)
-        if self.rnn_type == "LSTM":
-            output, (hidden1, hidden2) = self.rnn(emb, (hidden1, hidden2))
-        else:
-            output, hidden1 = self.rnn(emb, hidden1)
-
-        decoded = self.decoder(
-            output.contiguous().view(output.size(0) * output.size(1), output.size(2))
-        )
-        if self.rnn_type == "LSTM":
-            return (
-                decoded.view(output.size(0), output.size(1), decoded.size(1)),
-                hidden1,
-                hidden2,
-            )
-        else:
-            return (
-                decoded.view(output.size(0), output.size(1), decoded.size(1)),
-                hidden1,
-            )
-
-    def get_dummy_inputs(self):
-        tgt = torch.LongTensor([0, 1]).unsqueeze(0)
-        hidden = torch.randn(self.nlayers, 1, self.nhid)
-        if self.rnn_type == "LSTM":
-            return (tgt, hidden, hidden)
-        else:
-            return (tgt, hidden)
-
-    def get_input_names(self):
-        if self.rnn_type == "LSTM":
-            return ["x", "in_hidden1", "in_hidden2"]
-        else:
-            return ["x", "in_hidden1"]
-
-    def get_output_names(self):
-        if self.rnn_type == "LSTM":
-            return ["y", "out_hidden1", "out_hidden2"]
-        else:
-            return ["y", "out_hidden1"]
-
-    def get_dynamic_axes(self):
-        ret = {
-            "x": {0: "x_batch", 1: "x_length"},
-            "y": {0: "y_batch"},
-            "in_hidden1": {1: "hidden1_batch"},
-            "out_hidden1": {1: "out_hidden1_batch"},
-        }
-        if self.rnn_type == "LSTM":
-            ret.update(
-                {
-                    "in_hidden2": {1: "hidden2_batch"},
-                    "out_hidden2": {1: "out_hidden2_batch"},
-                }
-            )
-        return ret
-
-    def get_model_config(self, path):
-        return {
-            "use_lm": True,
-            "model_path": os.path.join(path, f"{self.model_name}.onnx"),
-            "lm_type": "SequentialRNNLM",
-            "rnn_type": self.rnn_type,
-            "nhid": self.nhid,
-            "nlayers": self.nlayers,
-        }

funasr/export/models/language_models/subsampling.py

@@ -1,185 +0,0 @@
-"""Subsampling layer definition."""
-
-import torch
-
-
-class OnnxConv2dSubsampling(torch.nn.Module):
-    """Convolutional 2D subsampling (to 1/4 length).
-
-    Args:
-        idim (int): Input dimension.
-        odim (int): Output dimension.
-        dropout_rate (float): Dropout rate.
-        pos_enc (torch.nn.Module): Custom position encoding layer.
-
-    """
-
-    def __init__(self, model):
-        """Construct an Conv2dSubsampling object."""
-        super().__init__()
-        self.conv = model.conv
-        self.out = model.out
-
-    def forward(self, x, x_mask):
-        """Subsample x.
-
-        Args:
-            x (torch.Tensor): Input tensor (#batch, time, idim).
-            x_mask (torch.Tensor): Input mask (#batch, 1, time).
-
-        Returns:
-            torch.Tensor: Subsampled tensor (#batch, time', odim),
-                where time' = time // 4.
-            torch.Tensor: Subsampled mask (#batch, 1, time'),
-                where time' = time // 4.
-
-        """
-        x = x.unsqueeze(1)  # (b, c, t, f)
-        x = self.conv(x)
-        b, c, t, f = x.size()
-        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
-        if x_mask is None:
-            return x, None
-        return x, x_mask[:, :-2:2][:, :-2:2]
-
-    def __getitem__(self, key):
-        """Get item.
-
-        When reset_parameters() is called, if use_scaled_pos_enc is used,
-            return the positioning encoding.
-
-        """
-        if key != -1:
-            raise NotImplementedError("Support only `-1` (for `reset_parameters`).")
-        return self.out[key]
-
-
-class OnnxConv2dSubsampling2(torch.nn.Module):
-    """Convolutional 2D subsampling (to 1/2 length).
-
-    Args:
-        idim (int): Input dimension.
-        odim (int): Output dimension.
-        dropout_rate (float): Dropout rate.
-        pos_enc (torch.nn.Module): Custom position encoding layer.
-
-    """
-
-    def __init__(self, model):
-        """Construct an Conv2dSubsampling object."""
-        super().__init__()
-        self.conv = model.conv
-        self.out = model.out
-
-    def forward(self, x, x_mask):
-        """Subsample x.
-
-        Args:
-            x (torch.Tensor): Input tensor (#batch, time, idim).
-            x_mask (torch.Tensor): Input mask (#batch, 1, time).
-
-        Returns:
-            torch.Tensor: Subsampled tensor (#batch, time', odim),
-                where time' = time // 2.
-            torch.Tensor: Subsampled mask (#batch, 1, time'),
-                where time' = time // 2.
-
-        """
-        x = x.unsqueeze(1)  # (b, c, t, f)
-        x = self.conv(x)
-        b, c, t, f = x.size()
-        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
-        if x_mask is None:
-            return x, None
-        return x, x_mask[:, :-2:2][:, :-2:1]
-
-    def __getitem__(self, key):
-        """Get item.
-
-        When reset_parameters() is called, if use_scaled_pos_enc is used,
-            return the positioning encoding.
-
-        """
-        if key != -1:
-            raise NotImplementedError("Support only `-1` (for `reset_parameters`).")
-        return self.out[key]
-
-
-class OnnxConv2dSubsampling6(torch.nn.Module):
-    """Convolutional 2D subsampling (to 1/6 length).
-
-    Args:
-        idim (int): Input dimension.
-        odim (int): Output dimension.
-        dropout_rate (float): Dropout rate.
-        pos_enc (torch.nn.Module): Custom position encoding layer.
-
-    """
-
-    def __init__(self, model):
-        """Construct an Conv2dSubsampling object."""
-        super().__init__()
-        self.conv = model.conv
-        self.out = model.out
-
-    def forward(self, x, x_mask):
-        """Subsample x.
-
-        Args:
-            x (torch.Tensor): Input tensor (#batch, time, idim).
-            x_mask (torch.Tensor): Input mask (#batch, 1, time).
-
-        Returns:
-            torch.Tensor: Subsampled tensor (#batch, time', odim),
-                where time' = time // 6.
-            torch.Tensor: Subsampled mask (#batch, 1, time'),
-                where time' = time // 6.
-
-        """
-        x = x.unsqueeze(1)  # (b, c, t, f)
-        x = self.conv(x)
-        b, c, t, f = x.size()
-        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
-        if x_mask is None:
-            return x, None
-        return x, x_mask[:, :-2:2][:, :-4:3]
-
-
-class OnnxConv2dSubsampling8(torch.nn.Module):
-    """Convolutional 2D subsampling (to 1/8 length).
-
-    Args:
-        idim (int): Input dimension.
-        odim (int): Output dimension.
-        dropout_rate (float): Dropout rate.
-        pos_enc (torch.nn.Module): Custom position encoding layer.
-
-    """
-
-    def __init__(self, model):
-        """Construct an Conv2dSubsampling object."""
-        super().__init__()
-        self.conv = model.conv
-        self.out = model.out
-
-    def forward(self, x, x_mask):
-        """Subsample x.
-
-        Args:
-            x (torch.Tensor): Input tensor (#batch, time, idim).
-            x_mask (torch.Tensor): Input mask (#batch, 1, time).
-
-        Returns:
-            torch.Tensor: Subsampled tensor (#batch, time', odim),
-                where time' = time // 8.
-            torch.Tensor: Subsampled mask (#batch, 1, time'),
-                where time' = time // 8.
-
-        """
-        x = x.unsqueeze(1)  # (b, c, t, f)
-        x = self.conv(x)
-        b, c, t, f = x.size()
-        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
-        if x_mask is None:
-            return x, None
-        return x, x_mask[:, :-2:2][:, :-2:2][:, :-2:2]

funasr/export/models/language_models/transformer.py

@@ -1,110 +0,0 @@
-import os
-
-import torch
-import torch.nn as nn
-from funasr.modules.vgg2l import import VGG2L
-from funasr.modules.attention import MultiHeadedAttention
-from funasr.modules.subsampling import (
-    Conv2dSubsampling, Conv2dSubsampling6, Conv2dSubsampling8)
-
-from funasr.export.models.modules.encoder_layer import EncoderLayerConformer as OnnxEncoderLayer
-from funasr.export.models.language_models.embed import Embedding
-from funasr.export.models.modules.multihead_att import OnnxMultiHeadedAttention
-
-from funasr.export.utils.torch_function import MakePadMask
-
-class TransformerLM(nn.Module, AbsExportModel):
-    def __init__(self, model, max_seq_len=512, **kwargs):
-        super().__init__()
-        self.embed = Embedding(model.embed, max_seq_len)
-        self.encoder = model.encoder
-        self.decoder = model.decoder
-        self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
-        # replace multihead attention module into customized module.
-        for i, d in enumerate(self.encoder.encoders):
-            # d is EncoderLayer
-            if isinstance(d.self_attn, MultiHeadedAttention):
-                d.self_attn = OnnxMultiHeadedAttention(d.self_attn)
-            self.encoder.encoders[i] = OnnxEncoderLayer(d)
-
-        self.model_name = "transformer_lm"
-        self.num_heads = self.encoder.encoders[0].self_attn.h
-        self.hidden_size = self.encoder.encoders[0].self_attn.linear_out.out_features
-
-    def prepare_mask(self, mask):
-        if len(mask.shape) == 2:
-            mask = mask[:, None, None, :]
-        elif len(mask.shape) == 3:
-            mask = mask[:, None, :]
-        mask = 1 - mask
-        return mask * -10000.0
-
-    def forward(self, y, cache):
-        feats_length = torch.ones(y.shape).sum(dim=-1).type(torch.long)
-        mask = self.make_pad_mask(feats_length)  # (B, T)
-        mask = (y != 0) * mask
-
-        xs = self.embed(y)
-        # forward_one_step of Encoder
-        if isinstance(
-            self.encoder.embed,
-            (Conv2dSubsampling, Conv2dSubsampling6, Conv2dSubsampling8, VGG2L),
-        ):
-            xs, mask = self.encoder.embed(xs, mask)
-        else:
-            xs = self.encoder.embed(xs)
-
-        new_cache = []
-        mask = self.prepare_mask(mask)
-        for c, e in zip(cache, self.encoder.encoders):
-            xs, mask = e(xs, mask, c)
-            new_cache.append(xs)
-
-        if self.encoder.normalize_before:
-            xs = self.encoder.after_norm(xs)
-
-        h = self.decoder(xs[:, -1])
-        return h, new_cache
-
-    def get_dummy_inputs(self):
-        tgt = torch.LongTensor([1]).unsqueeze(0)
-        cache = [
-            torch.zeros((1, 1, self.encoder.encoders[0].size))
-            for _ in range(len(self.encoder.encoders))
-        ]
-        return (tgt, cache)
-
-    def is_optimizable(self):
-        return True
-
-    def get_input_names(self):
-        return ["tgt"] + ["cache_%d" % i for i in range(len(self.encoder.encoders))]
-
-    def get_output_names(self):
-        return ["y"] + ["out_cache_%d" % i for i in range(len(self.encoder.encoders))]
-
-    def get_dynamic_axes(self):
-        ret = {"tgt": {0: "tgt_batch", 1: "tgt_length"}}
-        ret.update(
-            {
-                "cache_%d" % d: {0: "cache_%d_batch" % d, 1: "cache_%d_length" % d}
-                for d in range(len(self.encoder.encoders))
-            }
-        )
-        ret.update(
-            {
-                "out_cache_%d"
-                % d: {0: "out_cache_%d_batch" % d, 1: "out_cache_%d_length" % d}
-                for d in range(len(self.encoder.encoders))
-            }
-        )
-        return ret
-
-    def get_model_config(self, path):
-        return {
-            "use_lm": True,
-            "model_path": os.path.join(path, f"{self.model_name}.onnx"),
-            "lm_type": "TransformerLM",
-            "odim": self.encoder.encoders[0].size,
-            "nlayers": len(self.encoder.encoders),
-        }

funasr/fileio/sound_scp.py

@@ -4,7 +4,7 @@ from typing import List, Tuple, Union
 
 import random
 import numpy as np
-import soundfile
+import librosa
 import librosa
 
 import torch
@@ -116,7 +116,7 @@ class SoundScpReader(collections.abc.Mapping):
     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 Tuple
 from typing import Union
 
 import torch
-from torch_complex import functional as FC
-from torch_complex.tensor import ComplexTensor
+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 Tuple
 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
-
-from rotary_embedding_torch import RotaryEmbedding
+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 logging
 import humanfriendly
 import numpy as np
 import torch
-from torch_complex.tensor import ComplexTensor
+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 os
 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 @@ def load_wav(wav_rxfilename, start=0, end=None):
         # 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

@@ -1 +0,0 @@
-"""Initialize sub package."""

funasr/modules/frontends/beamformer.py

@@ -1,84 +0,0 @@
-import torch
-from torch_complex import functional as FC
-from torch_complex.tensor import ComplexTensor
-
-
-def get_power_spectral_density_matrix(
-    xs: ComplexTensor, mask: torch.Tensor, normalization=True, eps: float = 1e-15
-) -> ComplexTensor:
-    """Return cross-channel power spectral density (PSD) matrix
-
-    Args:
-        xs (ComplexTensor): (..., F, C, T)
-        mask (torch.Tensor): (..., F, C, T)
-        normalization (bool):
-        eps (float):
-    Returns
-        psd (ComplexTensor): (..., F, C, C)
-
-    """
-    # outer product: (..., C_1, T) x (..., C_2, T) -> (..., T, C, C_2)
-    psd_Y = FC.einsum("...ct,...et->...tce", [xs, xs.conj()])
-
-    # Averaging mask along C: (..., C, T) -> (..., T)
-    mask = mask.mean(dim=-2)
-
-    # Normalized mask along T: (..., T)
-    if normalization:
-        # If assuming the tensor is padded with zero, the summation along
-        # the time axis is same regardless of the padding length.
-        mask = mask / (mask.sum(dim=-1, keepdim=True) + eps)
-
-    # psd: (..., T, C, C)
-    psd = psd_Y * mask[..., None, None]
-    # (..., T, C, C) -> (..., C, C)
-    psd = psd.sum(dim=-3)
-
-    return psd
-
-
-def get_mvdr_vector(
-    psd_s: ComplexTensor,
-    psd_n: ComplexTensor,
-    reference_vector: torch.Tensor,
-    eps: float = 1e-15,
-) -> ComplexTensor:
-    """Return the MVDR(Minimum Variance Distortionless Response) vector:
-
-        h = (Npsd^-1 @ Spsd) / (Tr(Npsd^-1 @ Spsd)) @ u
-
-    Reference:
-        On optimal frequency-domain multichannel linear filtering
-        for noise reduction; M. Souden et al., 2010;
-        https://ieeexplore.ieee.org/document/5089420
-
-    Args:
-        psd_s (ComplexTensor): (..., F, C, C)
-        psd_n (ComplexTensor): (..., F, C, C)
-        reference_vector (torch.Tensor): (..., C)
-        eps (float):
-    Returns:
-        beamform_vector (ComplexTensor)r: (..., F, C)
-    """
-    # Add eps
-    C = psd_n.size(-1)
-    eye = torch.eye(C, dtype=psd_n.dtype, device=psd_n.device)
-    shape = [1 for _ in range(psd_n.dim() - 2)] + [C, C]
-    eye = eye.view(*shape)
-    psd_n += eps * eye
-
-    # numerator: (..., C_1, C_2) x (..., C_2, C_3) -> (..., C_1, C_3)
-    numerator = FC.einsum("...ec,...cd->...ed", [psd_n.inverse(), psd_s])
-    # ws: (..., C, C) / (...,) -> (..., C, C)
-    ws = numerator / (FC.trace(numerator)[..., None, None] + eps)
-    # h: (..., F, C_1, C_2) x (..., C_2) -> (..., F, C_1)
-    beamform_vector = FC.einsum("...fec,...c->...fe", [ws, reference_vector])
-    return beamform_vector
-
-
-def apply_beamforming_vector(
-    beamform_vector: ComplexTensor, mix: ComplexTensor
-) -> ComplexTensor:
-    # (..., C) x (..., C, T) -> (..., T)
-    es = FC.einsum("...c,...ct->...t", [beamform_vector.conj(), mix])
-    return es

funasr/modules/frontends/dnn_beamformer.py

@@ -1,172 +0,0 @@
-"""DNN beamformer module."""
-from typing import Tuple
-
-import torch
-from torch.nn import functional as F
-
-from funasr.modules.frontends.beamformer import apply_beamforming_vector
-from funasr.modules.frontends.beamformer import get_mvdr_vector
-from funasr.modules.frontends.beamformer import (
-    get_power_spectral_density_matrix,  # noqa: H301
-)
-from funasr.modules.frontends.mask_estimator import MaskEstimator
-from torch_complex.tensor import ComplexTensor
-
-
-class DNN_Beamformer(torch.nn.Module):
-    """DNN mask based Beamformer
-
-    Citation:
-        Multichannel End-to-end Speech Recognition; T. Ochiai et al., 2017;
-        https://arxiv.org/abs/1703.04783
-
-    """
-
-    def __init__(
-        self,
-        bidim,
-        btype="blstmp",
-        blayers=3,
-        bunits=300,
-        bprojs=320,
-        bnmask=2,
-        dropout_rate=0.0,
-        badim=320,
-        ref_channel: int = -1,
-        beamformer_type="mvdr",
-    ):
-        super().__init__()
-        self.mask = MaskEstimator(
-            btype, bidim, blayers, bunits, bprojs, dropout_rate, nmask=bnmask
-        )
-        self.ref = AttentionReference(bidim, badim)
-        self.ref_channel = ref_channel
-
-        self.nmask = bnmask
-
-        if beamformer_type != "mvdr":
-            raise ValueError(
-                "Not supporting beamformer_type={}".format(beamformer_type)
-            )
-        self.beamformer_type = beamformer_type
-
-    def forward(
-        self, data: ComplexTensor, ilens: torch.LongTensor
-    ) -> Tuple[ComplexTensor, torch.LongTensor, ComplexTensor]:
-        """The forward function
-
-        Notation:
-            B: Batch
-            C: Channel
-            T: Time or Sequence length
-            F: Freq
-
-        Args:
-            data (ComplexTensor): (B, T, C, F)
-            ilens (torch.Tensor): (B,)
-        Returns:
-            enhanced (ComplexTensor): (B, T, F)
-            ilens (torch.Tensor): (B,)
-
-        """
-
-        def apply_beamforming(data, ilens, psd_speech, psd_noise):
-            # u: (B, C)
-            if self.ref_channel < 0:
-                u, _ = self.ref(psd_speech, ilens)
-            else:
-                # (optional) Create onehot vector for fixed reference microphone
-                u = torch.zeros(
-                    *(data.size()[:-3] + (data.size(-2),)), device=data.device
-                )
-                u[..., self.ref_channel].fill_(1)
-
-            ws = get_mvdr_vector(psd_speech, psd_noise, u)
-            enhanced = apply_beamforming_vector(ws, data)
-
-            return enhanced, ws
-
-        # data (B, T, C, F) -> (B, F, C, T)
-        data = data.permute(0, 3, 2, 1)
-
-        # mask: (B, F, C, T)
-        masks, _ = self.mask(data, ilens)
-        assert self.nmask == len(masks)
-
-        if self.nmask == 2:  # (mask_speech, mask_noise)
-            mask_speech, mask_noise = masks
-
-            psd_speech = get_power_spectral_density_matrix(data, mask_speech)
-            psd_noise = get_power_spectral_density_matrix(data, mask_noise)
-
-            enhanced, ws = apply_beamforming(data, ilens, psd_speech, psd_noise)
-
-            # (..., F, T) -> (..., T, F)
-            enhanced = enhanced.transpose(-1, -2)
-            mask_speech = mask_speech.transpose(-1, -3)
-        else:  # multi-speaker case: (mask_speech1, ..., mask_noise)
-            mask_speech = list(masks[:-1])
-            mask_noise = masks[-1]
-
-            psd_speeches = [
-                get_power_spectral_density_matrix(data, mask) for mask in mask_speech
-            ]
-            psd_noise = get_power_spectral_density_matrix(data, mask_noise)
-
-            enhanced = []
-            ws = []
-            for i in range(self.nmask - 1):
-                psd_speech = psd_speeches.pop(i)
-                # treat all other speakers' psd_speech as noises
-                enh, w = apply_beamforming(
-                    data, ilens, psd_speech, sum(psd_speeches) + psd_noise
-                )
-                psd_speeches.insert(i, psd_speech)
-
-                # (..., F, T) -> (..., T, F)
-                enh = enh.transpose(-1, -2)
-                mask_speech[i] = mask_speech[i].transpose(-1, -3)
-
-                enhanced.append(enh)
-                ws.append(w)
-
-        return enhanced, ilens, mask_speech
-
-
-class AttentionReference(torch.nn.Module):
-    def __init__(self, bidim, att_dim):
-        super().__init__()
-        self.mlp_psd = torch.nn.Linear(bidim, att_dim)
-        self.gvec = torch.nn.Linear(att_dim, 1)
-
-    def forward(
-        self, psd_in: ComplexTensor, ilens: torch.LongTensor, scaling: float = 2.0
-    ) -> Tuple[torch.Tensor, torch.LongTensor]:
-        """The forward function
-
-        Args:
-            psd_in (ComplexTensor): (B, F, C, C)
-            ilens (torch.Tensor): (B,)
-            scaling (float):
-        Returns:
-            u (torch.Tensor): (B, C)
-            ilens (torch.Tensor): (B,)
-        """
-        B, _, C = psd_in.size()[:3]
-        assert psd_in.size(2) == psd_in.size(3), psd_in.size()
-        # psd_in: (B, F, C, C)
-        psd = psd_in.masked_fill(
-            torch.eye(C, dtype=torch.bool, device=psd_in.device), 0
-        )
-        # psd: (B, F, C, C) -> (B, C, F)
-        psd = (psd.sum(dim=-1) / (C - 1)).transpose(-1, -2)
-
-        # Calculate amplitude
-        psd_feat = (psd.real**2 + psd.imag**2) ** 0.5
-
-        # (B, C, F) -> (B, C, F2)
-        mlp_psd = self.mlp_psd(psd_feat)
-        # (B, C, F2) -> (B, C, 1) -> (B, C)
-        e = self.gvec(torch.tanh(mlp_psd)).squeeze(-1)
-        u = F.softmax(scaling * e, dim=-1)
-        return u, ilens

funasr/modules/frontends/dnn_wpe.py

@@ -1,93 +0,0 @@
-from typing import Tuple
-
-from pytorch_wpe import wpe_one_iteration
-import torch
-from torch_complex.tensor import ComplexTensor
-
-from funasr.modules.frontends.mask_estimator import MaskEstimator
-from funasr.modules.nets_utils import make_pad_mask
-
-
-class DNN_WPE(torch.nn.Module):
-    def __init__(
-        self,
-        wtype: str = "blstmp",
-        widim: int = 257,
-        wlayers: int = 3,
-        wunits: int = 300,
-        wprojs: int = 320,
-        dropout_rate: float = 0.0,
-        taps: int = 5,
-        delay: int = 3,
-        use_dnn_mask: bool = True,
-        iterations: int = 1,
-        normalization: bool = False,
-    ):
-        super().__init__()
-        self.iterations = iterations
-        self.taps = taps
-        self.delay = delay
-
-        self.normalization = normalization
-        self.use_dnn_mask = use_dnn_mask
-
-        self.inverse_power = True
-
-        if self.use_dnn_mask:
-            self.mask_est = MaskEstimator(
-                wtype, widim, wlayers, wunits, wprojs, dropout_rate, nmask=1
-            )
-
-    def forward(
-        self, data: ComplexTensor, ilens: torch.LongTensor
-    ) -> Tuple[ComplexTensor, torch.LongTensor, ComplexTensor]:
-        """The forward function
-
-        Notation:
-            B: Batch
-            C: Channel
-            T: Time or Sequence length
-            F: Freq or Some dimension of the feature vector
-
-        Args:
-            data: (B, C, T, F)
-            ilens: (B,)
-        Returns:
-            data: (B, C, T, F)
-            ilens: (B,)
-        """
-        # (B, T, C, F) -> (B, F, C, T)
-        enhanced = data = data.permute(0, 3, 2, 1)
-        mask = None
-
-        for i in range(self.iterations):
-            # Calculate power: (..., C, T)
-            power = enhanced.real**2 + enhanced.imag**2
-            if i == 0 and self.use_dnn_mask:
-                # mask: (B, F, C, T)
-                (mask,), _ = self.mask_est(enhanced, ilens)
-                if self.normalization:
-                    # Normalize along T
-                    mask = mask / mask.sum(dim=-1)[..., None]
-                # (..., C, T) * (..., C, T) -> (..., C, T)
-                power = power * mask
-
-            # Averaging along the channel axis: (..., C, T) -> (..., T)
-            power = power.mean(dim=-2)
-
-            # enhanced: (..., C, T) -> (..., C, T)
-            enhanced = wpe_one_iteration(
-                data.contiguous(),
-                power,
-                taps=self.taps,
-                delay=self.delay,
-                inverse_power=self.inverse_power,
-            )
-
-            enhanced.masked_fill_(make_pad_mask(ilens, enhanced.real), 0)
-
-        # (B, F, C, T) -> (B, T, C, F)
-        enhanced = enhanced.permute(0, 3, 2, 1)
-        if mask is not None:
-            mask = mask.transpose(-1, -3)
-        return enhanced, ilens, mask

funasr/modules/frontends/feature_transform.py

@@ -1,263 +0,0 @@
-from typing import List
-from typing import Tuple
-from typing import Union
-
-import librosa
-import numpy as np
-import torch
-from torch_complex.tensor import ComplexTensor
-
-from funasr.modules.nets_utils import make_pad_mask
-
-
-class FeatureTransform(torch.nn.Module):
-    def __init__(
-        self,
-        # Mel options,
-        fs: int = 16000,
-        n_fft: int = 512,
-        n_mels: int = 80,
-        fmin: float = 0.0,
-        fmax: float = None,
-        # Normalization
-        stats_file: str = None,
-        apply_uttmvn: bool = True,
-        uttmvn_norm_means: bool = True,
-        uttmvn_norm_vars: bool = False,
-    ):
-        super().__init__()
-        self.apply_uttmvn = apply_uttmvn
-
-        self.logmel = LogMel(fs=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
-        self.stats_file = stats_file
-        if stats_file is not None:
-            self.global_mvn = GlobalMVN(stats_file)
-        else:
-            self.global_mvn = None
-
-        if self.apply_uttmvn is not None:
-            self.uttmvn = UtteranceMVN(
-                norm_means=uttmvn_norm_means, norm_vars=uttmvn_norm_vars
-            )
-        else:
-            self.uttmvn = None
-
-    def forward(
-        self, x: ComplexTensor, ilens: Union[torch.LongTensor, np.ndarray, List[int]]
-    ) -> Tuple[torch.Tensor, torch.LongTensor]:
-        # (B, T, F) or (B, T, C, F)
-        if x.dim() not in (3, 4):
-            raise ValueError(f"Input dim must be 3 or 4: {x.dim()}")
-        if not torch.is_tensor(ilens):
-            ilens = torch.from_numpy(np.asarray(ilens)).to(x.device)
-
-        if x.dim() == 4:
-            # h: (B, T, C, F) -> h: (B, T, F)
-            if self.training:
-                # Select 1ch randomly
-                ch = np.random.randint(x.size(2))
-                h = x[:, :, ch, :]
-            else:
-                # Use the first channel
-                h = x[:, :, 0, :]
-        else:
-            h = x
-
-        # h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F)
-        h = h.real**2 + h.imag**2
-
-        h, _ = self.logmel(h, ilens)
-        if self.stats_file is not None:
-            h, _ = self.global_mvn(h, ilens)
-        if self.apply_uttmvn:
-            h, _ = self.uttmvn(h, ilens)
-
-        return h, ilens
-
-
-class LogMel(torch.nn.Module):
-    """Convert STFT to fbank feats
-
-    The arguments is same as librosa.filters.mel
-
-    Args:
-        fs: number > 0 [scalar] sampling rate of the incoming signal
-        n_fft: int > 0 [scalar] number of FFT components
-        n_mels: int > 0 [scalar] number of Mel bands to generate
-        fmin: float >= 0 [scalar] lowest frequency (in Hz)
-        fmax: float >= 0 [scalar] highest frequency (in Hz).
-            If `None`, use `fmax = fs / 2.0`
-        htk: use HTK formula instead of Slaney
-        norm: {None, 1, np.inf} [scalar]
-            if 1, divide the triangular mel weights by the width of the mel band
-            (area normalization).  Otherwise, leave all the triangles aiming for
-            a peak value of 1.0
-
-    """
-
-    def __init__(
-        self,
-        fs: int = 16000,
-        n_fft: int = 512,
-        n_mels: int = 80,
-        fmin: float = 0.0,
-        fmax: float = None,
-        htk: bool = False,
-        norm=1,
-    ):
-        super().__init__()
-
-        _mel_options = dict(
-            sr=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax, htk=htk, norm=norm
-        )
-        self.mel_options = _mel_options
-
-        # Note(kamo): The mel matrix of librosa is different from kaldi.
-        melmat = librosa.filters.mel(**_mel_options)
-        # melmat: (D2, D1) -> (D1, D2)
-        self.register_buffer("melmat", torch.from_numpy(melmat.T).float())
-
-    def extra_repr(self):
-        return ", ".join(f"{k}={v}" for k, v in self.mel_options.items())
-
-    def forward(
-        self, feat: torch.Tensor, ilens: torch.LongTensor
-    ) -> Tuple[torch.Tensor, torch.LongTensor]:
-        # feat: (B, T, D1) x melmat: (D1, D2) -> mel_feat: (B, T, D2)
-        mel_feat = torch.matmul(feat, self.melmat)
-
-        logmel_feat = (mel_feat + 1e-20).log()
-        # Zero padding
-        logmel_feat = logmel_feat.masked_fill(make_pad_mask(ilens, logmel_feat, 1), 0.0)
-        return logmel_feat, ilens
-
-
-class GlobalMVN(torch.nn.Module):
-    """Apply global mean and variance normalization
-
-    Args:
-        stats_file(str): npy file of 1-dim array or text file.
-            From the _first element to
-            the {(len(array) - 1) / 2}th element are treated as
-            the sum of features,
-            and the rest excluding the last elements are
-            treated as the sum of the square value of features,
-            and the last elements eqauls to the number of samples.
-        std_floor(float):
-    """
-
-    def __init__(
-        self,
-        stats_file: str,
-        norm_means: bool = True,
-        norm_vars: bool = True,
-        eps: float = 1.0e-20,
-    ):
-        super().__init__()
-        self.norm_means = norm_means
-        self.norm_vars = norm_vars
-
-        self.stats_file = stats_file
-        stats = np.load(stats_file)
-
-        stats = stats.astype(float)
-        assert (len(stats) - 1) % 2 == 0, stats.shape
-
-        count = stats.flatten()[-1]
-        mean = stats[: (len(stats) - 1) // 2] / count
-        var = stats[(len(stats) - 1) // 2 : -1] / count - mean * mean
-        std = np.maximum(np.sqrt(var), eps)
-
-        self.register_buffer("bias", torch.from_numpy(-mean.astype(np.float32)))
-        self.register_buffer("scale", torch.from_numpy(1 / std.astype(np.float32)))
-
-    def extra_repr(self):
-        return (
-            f"stats_file={self.stats_file}, "
-            f"norm_means={self.norm_means}, norm_vars={self.norm_vars}"
-        )
-
-    def forward(
-        self, x: torch.Tensor, ilens: torch.LongTensor
-    ) -> Tuple[torch.Tensor, torch.LongTensor]:
-        # feat: (B, T, D)
-        if self.norm_means:
-            x += self.bias.type_as(x)
-            x.masked_fill(make_pad_mask(ilens, x, 1), 0.0)
-
-        if self.norm_vars:
-            x *= self.scale.type_as(x)
-        return x, ilens
-
-
-class UtteranceMVN(torch.nn.Module):
-    def __init__(
-        self, norm_means: bool = True, norm_vars: bool = False, eps: float = 1.0e-20
-    ):
-        super().__init__()
-        self.norm_means = norm_means
-        self.norm_vars = norm_vars
-        self.eps = eps
-
-    def extra_repr(self):
-        return f"norm_means={self.norm_means}, norm_vars={self.norm_vars}"
-
-    def forward(
-        self, x: torch.Tensor, ilens: torch.LongTensor
-    ) -> Tuple[torch.Tensor, torch.LongTensor]:
-        return utterance_mvn(
-            x, ilens, norm_means=self.norm_means, norm_vars=self.norm_vars, eps=self.eps
-        )
-
-
-def utterance_mvn(
-    x: torch.Tensor,
-    ilens: torch.LongTensor,
-    norm_means: bool = True,
-    norm_vars: bool = False,
-    eps: float = 1.0e-20,
-) -> Tuple[torch.Tensor, torch.LongTensor]:
-    """Apply utterance mean and variance normalization
-
-    Args:
-        x: (B, T, D), assumed zero padded
-        ilens: (B, T, D)
-        norm_means:
-        norm_vars:
-        eps:
-
-    """
-    ilens_ = ilens.type_as(x)
-    # mean: (B, D)
-    mean = x.sum(dim=1) / ilens_[:, None]
-
-    if norm_means:
-        x -= mean[:, None, :]
-        x_ = x
-    else:
-        x_ = x - mean[:, None, :]
-
-    # Zero padding
-    x_.masked_fill(make_pad_mask(ilens, x_, 1), 0.0)
-    if norm_vars:
-        var = x_.pow(2).sum(dim=1) / ilens_[:, None]
-        var = torch.clamp(var, min=eps)
-        x /= var.sqrt()[:, None, :]
-        x_ = x
-    return x_, ilens
-
-
-def feature_transform_for(args, n_fft):
-    return FeatureTransform(
-        # Mel options,
-        fs=args.fbank_fs,
-        n_fft=n_fft,
-        n_mels=args.n_mels,
-        fmin=args.fbank_fmin,
-        fmax=args.fbank_fmax,
-        # Normalization
-        stats_file=args.stats_file,
-        apply_uttmvn=args.apply_uttmvn,
-        uttmvn_norm_means=args.uttmvn_norm_means,
-        uttmvn_norm_vars=args.uttmvn_norm_vars,
-    )

funasr/modules/frontends/frontend.py

@@ -1,151 +0,0 @@
-from typing import List
-from typing import Optional
-from typing import Tuple
-from typing import Union
-
-import numpy
-import torch
-import torch.nn as nn
-from torch_complex.tensor import ComplexTensor
-
-from funasr.modules.frontends.dnn_beamformer import DNN_Beamformer
-from funasr.modules.frontends.dnn_wpe import DNN_WPE
-
-
-class Frontend(nn.Module):
-    def __init__(
-        self,
-        idim: int,
-        # WPE options
-        use_wpe: bool = False,
-        wtype: str = "blstmp",
-        wlayers: int = 3,
-        wunits: int = 300,
-        wprojs: int = 320,
-        wdropout_rate: float = 0.0,
-        taps: int = 5,
-        delay: int = 3,
-        use_dnn_mask_for_wpe: bool = True,
-        # Beamformer options
-        use_beamformer: bool = False,
-        btype: str = "blstmp",
-        blayers: int = 3,
-        bunits: int = 300,
-        bprojs: int = 320,
-        bnmask: int = 2,
-        badim: int = 320,
-        ref_channel: int = -1,
-        bdropout_rate=0.0,
-    ):
-        super().__init__()
-
-        self.use_beamformer = use_beamformer
-        self.use_wpe = use_wpe
-        self.use_dnn_mask_for_wpe = use_dnn_mask_for_wpe
-        # use frontend for all the data,
-        # e.g. in the case of multi-speaker speech separation
-        self.use_frontend_for_all = bnmask > 2
-
-        if self.use_wpe:
-            if self.use_dnn_mask_for_wpe:
-                # Use DNN for power estimation
-                # (Not observed significant gains)
-                iterations = 1
-            else:
-                # Performing as conventional WPE, without DNN Estimator
-                iterations = 2
-
-            self.wpe = DNN_WPE(
-                wtype=wtype,
-                widim=idim,
-                wunits=wunits,
-                wprojs=wprojs,
-                wlayers=wlayers,
-                taps=taps,
-                delay=delay,
-                dropout_rate=wdropout_rate,
-                iterations=iterations,
-                use_dnn_mask=use_dnn_mask_for_wpe,
-            )
-        else:
-            self.wpe = None
-
-        if self.use_beamformer:
-            self.beamformer = DNN_Beamformer(
-                btype=btype,
-                bidim=idim,
-                bunits=bunits,
-                bprojs=bprojs,
-                blayers=blayers,
-                bnmask=bnmask,
-                dropout_rate=bdropout_rate,
-                badim=badim,
-                ref_channel=ref_channel,
-            )
-        else:
-            self.beamformer = None
-
-    def forward(
-        self, x: ComplexTensor, ilens: Union[torch.LongTensor, numpy.ndarray, List[int]]
-    ) -> Tuple[ComplexTensor, torch.LongTensor, Optional[ComplexTensor]]:
-        assert len(x) == len(ilens), (len(x), len(ilens))
-        # (B, T, F) or (B, T, C, F)
-        if x.dim() not in (3, 4):
-            raise ValueError(f"Input dim must be 3 or 4: {x.dim()}")
-        if not torch.is_tensor(ilens):
-            ilens = torch.from_numpy(numpy.asarray(ilens)).to(x.device)
-
-        mask = None
-        h = x
-        if h.dim() == 4:
-            if self.training:
-                choices = [(False, False)] if not self.use_frontend_for_all else []
-                if self.use_wpe:
-                    choices.append((True, False))
-
-                if self.use_beamformer:
-                    choices.append((False, True))
-
-                use_wpe, use_beamformer = choices[numpy.random.randint(len(choices))]
-
-            else:
-                use_wpe = self.use_wpe
-                use_beamformer = self.use_beamformer
-
-            # 1. WPE
-            if use_wpe:
-                # h: (B, T, C, F) -> h: (B, T, C, F)
-                h, ilens, mask = self.wpe(h, ilens)
-
-            # 2. Beamformer
-            if use_beamformer:
-                # h: (B, T, C, F) -> h: (B, T, F)
-                h, ilens, mask = self.beamformer(h, ilens)
-
-        return h, ilens, mask
-
-
-def frontend_for(args, idim):
-    return Frontend(
-        idim=idim,
-        # WPE options
-        use_wpe=args.use_wpe,
-        wtype=args.wtype,
-        wlayers=args.wlayers,
-        wunits=args.wunits,
-        wprojs=args.wprojs,
-        wdropout_rate=args.wdropout_rate,
-        taps=args.wpe_taps,
-        delay=args.wpe_delay,
-        use_dnn_mask_for_wpe=args.use_dnn_mask_for_wpe,
-        # Beamformer options
-        use_beamformer=args.use_beamformer,
-        btype=args.btype,
-        blayers=args.blayers,
-        bunits=args.bunits,
-        bprojs=args.bprojs,
-        bnmask=args.bnmask,
-        badim=args.badim,
-        ref_channel=args.ref_channel,
-        bdropout_rate=args.bdropout_rate,
-    )

funasr/modules/frontends/mask_estimator.py

@@ -1,77 +0,0 @@
-from typing import Tuple
-
-import numpy as np
-import torch
-from torch.nn import functional as F
-from torch_complex.tensor import ComplexTensor
-
-from funasr.modules.nets_utils import make_pad_mask
-from funasr.modules.rnn.encoders import RNN
-from funasr.modules.rnn.encoders import RNNP
-
-
-class MaskEstimator(torch.nn.Module):
-    def __init__(self, type, idim, layers, units, projs, dropout, nmask=1):
-        super().__init__()
-        subsample = np.ones(layers + 1, dtype=np.int32)
-
-        typ = type.lstrip("vgg").rstrip("p")
-        if type[-1] == "p":
-            self.brnn = RNNP(idim, layers, units, projs, subsample, dropout, typ=typ)
-        else:
-            self.brnn = RNN(idim, layers, units, projs, dropout, typ=typ)
-
-        self.type = type
-        self.nmask = nmask
-        self.linears = torch.nn.ModuleList(
-            [torch.nn.Linear(projs, idim) for _ in range(nmask)]
-        )
-
-    def forward(
-        self, xs: ComplexTensor, ilens: torch.LongTensor
-    ) -> Tuple[Tuple[torch.Tensor, ...], torch.LongTensor]:
-        """The forward function
-
-        Args:
-            xs: (B, F, C, T)
-            ilens: (B,)
-        Returns:
-            hs (torch.Tensor): The hidden vector (B, F, C, T)
-            masks: A tuple of the masks. (B, F, C, T)
-            ilens: (B,)
-        """
-        assert xs.size(0) == ilens.size(0), (xs.size(0), ilens.size(0))
-        _, _, C, input_length = xs.size()
-        # (B, F, C, T) -> (B, C, T, F)
-        xs = xs.permute(0, 2, 3, 1)
-
-        # Calculate amplitude: (B, C, T, F) -> (B, C, T, F)
-        xs = (xs.real**2 + xs.imag**2) ** 0.5
-        # xs: (B, C, T, F) -> xs: (B * C, T, F)
-        xs = xs.contiguous().view(-1, xs.size(-2), xs.size(-1))
-        # ilens: (B,) -> ilens_: (B * C)
-        ilens_ = ilens[:, None].expand(-1, C).contiguous().view(-1)
-
-        # xs: (B * C, T, F) -> xs: (B * C, T, D)
-        xs, _, _ = self.brnn(xs, ilens_)
-        # xs: (B * C, T, D) -> xs: (B, C, T, D)
-        xs = xs.view(-1, C, xs.size(-2), xs.size(-1))
-
-        masks = []
-        for linear in self.linears:
-            # xs: (B, C, T, D) -> mask:(B, C, T, F)
-            mask = linear(xs)
-
-            mask = torch.sigmoid(mask)
-            # Zero padding
-            mask.masked_fill(make_pad_mask(ilens, mask, length_dim=2), 0)
-
-            # (B, C, T, F) -> (B, F, C, T)
-            mask = mask.permute(0, 3, 1, 2)
-
-            # Take cares of multi gpu cases: If input_length > max(ilens)
-            if mask.size(-1) < input_length:
-                mask = F.pad(mask, [0, input_length - mask.size(-1)], value=0)
-            masks.append(mask)
-
-        return tuple(masks), ilens

funasr/train/trainer.py

@@ -278,14 +278,11 @@ class Trainer:
         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 @@ import struct
 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 @@ def get_sr_from_wav(fname: str):
                     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 @@ from multiprocessing import Pool
 
 import kaldiio
 import numpy as np
-import soundfile
+import librosa
 import torch.distributed as dist
 import torchaudio
 
@@ -46,7 +46,7 @@ def wav2num_frame(wav_path, frontend_conf):
     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 @@ import os
 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 @@ def sv_preprocess(inputs: Union[np.ndarray, list]):
         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 codecs
 import logging
 import argparse
 import numpy as np
-import edit_distance
+# import edit_distance
 from itertools import zip_longest
 
 
@@ -160,112 +160,112 @@ def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocess
     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 @@ SUPPORTED_MODES = ['cal_aas', 'read_ext_alphas']
 
 
 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 librosa
 import numpy as np
 import torch
 import torchaudio
-import soundfile
+import librosa
 import torchaudio.compliance.kaldi as kaldi
 
 
@@ -166,7 +166,7 @@ def compute_fbank(wav_file,
         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 @@ def wav2num_frame(wav_path, frontend_conf):
     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 ffmpeg
 import numpy as np
 import torch
 import torch.nn.functional as F

setup.py

@@ -10,36 +10,36 @@ from setuptools import setup
 
 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 @@ setup(
     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="[email protected]",
     description="FunASR: A Fundamental End-to-End Speech Recognition Toolkit",
     long_description=open(os.path.join(dirname, "README.md"), encoding="utf-8").read(),