cli add ds2-librispeech offline, fix versionm, test=asr

paddlespeech/cli/asr/infer.py

@@ -91,6 +91,20 @@
  'lm_md5':
  '29e02312deb2e59b3c8686c7966d4fe3'
  },
+ "deepspeech2offline_librispeech-en-16k": {
+ 'url':
+ 'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz',
+ 'md5':
+ 'f5666c81ad015c8de03aac2bc92e5762',
+ 'cfg_path':
+ 'model.yaml',
+ 'ckpt_path':
+ 'exp/deepspeech2/checkpoints/avg_1',
+ 'lm_url':
+ 'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm',
+ 'lm_md5':
+ '099a601759d467cd0a8523ff939819c5'
+ },
 }
 
 model_alias = {
@@ -328,18 +342,15 @@ def infer(self, model_type: str):
  audio = self._inputs["audio"]
  audio_len = self._inputs["audio_len"]
  if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
- result_transcripts = self.model.decode(
- audio,
- audio_len,
- self.text_feature.vocab_list,
- decoding_method=cfg.decoding_method,
- lang_model_path=cfg.lang_model_path,
- beam_alpha=cfg.alpha,
- beam_beta=cfg.beta,
- beam_size=cfg.beam_size,
- cutoff_prob=cfg.cutoff_prob,
- cutoff_top_n=cfg.cutoff_top_n,
- num_processes=cfg.num_proc_bsearch)
+ decode_batch_size = audio.shape[0]
+ self.model.decoder.init_decoder(
+ decode_batch_size, self.text_feature.vocab_list,
+ cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta,
+ cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n,
+ cfg.num_proc_bsearch)
+
+ result_transcripts = self.model.decode(audio, audio_len)
+ self.model.decoder.del_decoder()
  self._outputs["result"] = result_transcripts[0]
 
  elif "conformer" in model_type or "transformer" in model_type:

paddlespeech/cli/utils.py

@@ -34,7 +34,7 @@
 try:
  from .. import __version__
 except ImportError:
- __version__ = 0.0.0 # for develop branch
+ __version__ = "0.0.0"  # for develop branch
 
 requests.adapters.DEFAULT_RETRIES = 3
 

paddlespeech/s2t/io/sampler.py

@@ -51,7 +51,7 @@ def _batch_shuffle(indices, batch_size, epoch, clipped=False):
  """
  rng = np.random.RandomState(epoch)
  shift_len = rng.randint(0, batch_size - 1)
- batch_indices = list(zip(* [iter(indices[shift_len:])] * batch_size))
+ batch_indices = list(zip(*[iter(indices[shift_len:])] * batch_size))
  rng.shuffle(batch_indices)
  batch_indices = [item for batch in batch_indices for item in batch]
  assert clipped is False

paddlespeech/t2s/modules/transformer/repeat.py

@@ -41,4 +41,4 @@ def repeat(N, fn):
  MultiSequential
  Repeated model instance.
  """
- return MultiSequential(* [fn(n) for n in range(N)])
+ return MultiSequential(*[fn(n) for n in range(N)])