funasr v2

游雁

2023-11-23 723488d97b256a2682af3bf8eb8a8da2c1a6990d

 funasr/datasets/dataset_jsonl.py

@@ -22,7 +22,10 @@

def extract_features(data, date_type: str="sound", frontend=None):
   if date_type == "sound":
      feat, feats_lens = frontend(data, len(data))
      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)
@@ -74,13 +77,14 @@


class AudioDataset(torch.utils.data.Dataset):
   def __init__(self, path, frontend=None, tokenizer=None):
   def __init__(self, path, frontend=None, tokenizer=None, token_id_converter=None):
      super().__init__()
      self.indexed_dataset = IndexedDatasetJsonl(path)
      self.frontend = frontend.forward
      self.fs = 16000 if frontend is None else frontend.fs
      self.data_type = "sound"
      self.tokenizer = tokenizer
      self.token_id_converter = token_id_converter
      self.int_pad_value = -1
      self.float_pad_value = 0.0

@@ -92,13 +96,15 @@
   
   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)
      target = item["target"]
      text = self.tokenizer.encode(target)
      text_lengths = len(text)
      text, text_lengths = torch.tensor(text, dtype=torch.int64), torch.tensor([text_lengths], dtype=torch.int32)
      text = self.tokenizer.text2tokens(target)
      ids = self.token_id_converter.tokens2ids(text)
      ids_lengths = len(ids)
      text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)
      return {"speech": speech,
              "speech_lengths": speech_lengths,
              "text": text,
@@ -108,17 +114,19 @@
   
   def collator(self, samples: list=None):
      
      # return samples
		
      outputs = {}
      for sample in samples:
         for key in sample.keys():
            if key not in outputs:
               outputs[key] = []
            outputs[key].append(sample[key])
		

      for key, data_list in outputs.items():
         if data_list[0].dtype.kind == "i":
         if data_list[0].dtype == torch.int64:
            pad_value = self.int_pad_value
         else:
            pad_value = self.float_pad_value
         outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
      return samples
      return outputs