funasr2 paraformer biciparaformer contextuaparaformer

游雁

2023-12-13 806a03609df033d61f824f1ab8527eb88fe837ad

 funasr/datasets/dataset_jsonl.py

@@ -8,33 +8,7 @@
import time
import logging

def load_audio(audio_path: str, fs: int=16000):
   audio = None
   if audio_path.startswith("oss:"):
      pass
   elif audio_path.startswith("odps:"):
      pass
   else:
      if ".ark:" in audio_path:
         audio = kaldiio.load_mat(audio_path)
      else:
         # audio, fs = librosa.load(audio_path, sr=fs)
         audio, fs = torchaudio.load(audio_path)
         audio = audio[0, :]
   return audio

def extract_features(data, date_type: str="sound", frontend=None):
   if date_type == "sound":

      if isinstance(data, np.ndarray):
         data = torch.from_numpy(data).to(torch.float32)
      data_len = torch.tensor([data.shape[0]]).to(torch.int32)
      feat, feats_lens = frontend(data[None, :], data_len)

      feat = feat[0, :, :]
   else:
      feat, feats_lens = torch.from_numpy(data).to(torch.float32), torch.tensor([data.shape[0]]).to(torch.int32)
   return feat, feats_lens
from funasr.datasets.fun_datasets.load_audio_extract_fbank import load_audio, extract_fbank
   
   

@@ -115,17 +89,16 @@
   
   def __getitem__(self, index):
      item = self.indexed_dataset[index]
      # return item

      source = item["source"]
      data_src = load_audio(source, fs=self.fs)
      speech, speech_lengths = extract_features(data_src, self.data_type, self.frontend)
      speech, speech_lengths = extract_fbank(data_src, self.data_type, self.frontend) # speech: [b, T, d]
      target = item["target"]
      ids = self.tokenizer.encode(target)
      ids_lengths = len(ids)
      text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)

      return {"speech": speech,
      return {"speech": speech[0, :, :],
              "speech_lengths": speech_lengths,
              "text": text,
              "text_lengths": text_lengths,