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

paddlespeech/cli/asr/infer.py

@@ -91,6 +91,20 @@ pretrained_models = {
         '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 @@ class ASRExecutor(BaseExecutor):
         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 @@ from .entry import commands
 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)])