funasr2 paraformer biciparaformer contextuaparaformer

游雁

2023-12-13 806a03609df033d61f824f1ab8527eb88fe837ad

 funasr/datasets/dataset_jsonl.py

@@ -6,34 +6,9 @@
import librosa
import torchaudio
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
   
   

@@ -41,8 +16,7 @@
   
   def __init__(self, path):
      super().__init__()
      # data_parallel_size = dist.get_world_size()
      data_parallel_size = 1
		
      contents = []
      with open(path, encoding='utf-8') as fin:
         for line in fin:
@@ -66,33 +40,46 @@
      
      self.contents = []
      total_num = len(contents)
      num_per_rank = total_num // data_parallel_size
      # rank = dist.get_rank()
      rank = 0
      try:
         rank = dist.get_rank()
         world_size = dist.get_world_size()
      except:
         rank = 0
         world_size = 1
         logging.warning("distributed is not initialized, only single shard")
      num_per_rank = total_num // world_size
		
      # rank = 0
      # import ipdb; ipdb.set_trace()
      self.contents = contents[rank * num_per_rank:(rank + 1) * num_per_rank]

	
      logging.info("in rank: {}, num of samplers: {}, total_num of samplers across ranks: {}".format(rank, len(self.contents), len(contents)))

   def __len__(self):
      return len(self.contents)
   
   def __getitem__(self, index):
      return self.contents[index]
	
   def get_source_len(self, data_dict):
      return data_dict["source_len"]

   def get_target_len(self, data_dict):
		
      return data_dict["target_len"] if "target_len" in data_dict else 0


class AudioDataset(torch.utils.data.Dataset):
   def __init__(self, path, frontend=None, tokenizer=None, token_id_converter=None):

   def __init__(self, path, frontend=None, tokenizer=None, int_pad_value: int = -1, float_pad_value: float = 0.0, **kwargs):
      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
      self.int_pad_value = int_pad_value
      self.float_pad_value = float_pad_value

   

@@ -102,18 +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"]
      text = self.tokenizer.text2tokens(target)
      ids = self.token_id_converter.tokens2ids(text)
      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,