Add Deepspeech2 online and offline in cli

paddlespeech/cli/asr/infer.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import argparse
-import io
 import os
 import sys
 from typing import List
@@ -23,9 +22,9 @@ import librosa
 import numpy as np
 import paddle
 import soundfile
-import yaml
 from yacs.config import CfgNode
 
+from ..download import get_path_from_url
 from ..executor import BaseExecutor
 from ..log import logger
 from ..utils import cli_register
@@ -64,14 +63,47 @@ pretrained_models = {
         'ckpt_path':
         'exp/transformer/checkpoints/avg_10',
     },
+    "deepspeech2offline_aishell-zh-16k": {
+        'url':
+        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
+        'md5':
+        '932c3593d62fe5c741b59b31318aa314',
+        'cfg_path':
+        'model.yaml',
+        'ckpt_path':
+        'exp/deepspeech2/checkpoints/avg_1',
+        'lm_url':
+        'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
+        'lm_md5':
+        '29e02312deb2e59b3c8686c7966d4fe3'
+    },
+    "deepspeech2online_aishell-zh-16k": {
+        'url':
+        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.1.1.model.tar.gz',
+        'md5':
+        'd5e076217cf60486519f72c217d21b9b',
+        'cfg_path':
+        'model.yaml',
+        'ckpt_path':
+        'exp/deepspeech2_online/checkpoints/avg_1',
+        'lm_url':
+        'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
+        'lm_md5':
+        '29e02312deb2e59b3c8686c7966d4fe3'
+    },
 }
 
 model_alias = {
-    "deepspeech2offline": "paddlespeech.s2t.models.ds2:DeepSpeech2Model",
-    "deepspeech2online": "paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline",
-    "conformer": "paddlespeech.s2t.models.u2:U2Model",
-    "transformer": "paddlespeech.s2t.models.u2:U2Model",
-    "wenetspeech": "paddlespeech.s2t.models.u2:U2Model",
+    "deepspeech2offline":
+    "paddlespeech.s2t.models.ds2:DeepSpeech2Model",
+    "deepspeech2online":
+    "paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline",
+    "conformer":
+    "paddlespeech.s2t.models.u2:U2Model",
+    "transformer":
+    "paddlespeech.s2t.models.u2:U2Model",
+    "wenetspeech":
+    "paddlespeech.s2t.models.u2:U2Model",
 }
 
 
@@ -95,7 +127,8 @@ class ASRExecutor(BaseExecutor):
             '--lang',
             type=str,
             default='zh',
-            help='Choose model language. zh or en, zh:[conformer_wenetspeech-zh-16k], en:[transformer_librispeech-en-16k]')
+            help='Choose model language. zh or en, zh:[conformer_wenetspeech-zh-16k], en:[transformer_librispeech-en-16k]'
+        )
         self.parser.add_argument(
             "--sample_rate",
             type=int,
@@ -111,7 +144,10 @@ class ASRExecutor(BaseExecutor):
             '--decode_method',
             type=str,
             default='attention_rescoring',
-            choices=['ctc_greedy_search', 'ctc_prefix_beam_search', 'attention', 'attention_rescoring'],
+            choices=[
+                'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention',
+                'attention_rescoring'
+            ],
             help='only support transformer and conformer model')
         self.parser.add_argument(
             '--ckpt_path',
@@ -187,13 +223,21 @@ class ASRExecutor(BaseExecutor):
             if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
                 from paddlespeech.s2t.io.collator import SpeechCollator
                 self.vocab = self.config.vocab_filepath
-                self.config.decode.lang_model_path = os.path.join(res_path, self.config.decode.lang_model_path)
+                self.config.decode.lang_model_path = os.path.join(
+                    MODEL_HOME, 'language_model',
+                    self.config.decode.lang_model_path)
                 self.collate_fn_test = SpeechCollator.from_config(self.config)
                 self.text_feature = TextFeaturizer(
-                    unit_type=self.config.unit_type,
-                    vocab=self.vocab)
+                    unit_type=self.config.unit_type, vocab=self.vocab)
+                lm_url = pretrained_models[tag]['lm_url']
+                lm_md5 = pretrained_models[tag]['lm_md5']
+                self.download_lm(
+                    lm_url,
+                    os.path.dirname(self.config.decode.lang_model_path), lm_md5)
+
             elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
-                self.config.spm_model_prefix = os.path.join(self.res_path, self.config.spm_model_prefix)
+                self.config.spm_model_prefix = os.path.join(
+                    self.res_path, self.config.spm_model_prefix)
                 self.text_feature = TextFeaturizer(
                     unit_type=self.config.unit_type,
                     vocab=self.config.vocab_filepath,
@@ -319,6 +363,13 @@ class ASRExecutor(BaseExecutor):
         """
         return self._outputs["result"]
 
+    def download_lm(self, url, lm_dir, md5sum):
+        download_path = get_path_from_url(
+            url=url,
+            root_dir=lm_dir,
+            md5sum=md5sum,
+            decompress=False, )
+
     def _pcm16to32(self, audio):
         assert (audio.dtype == np.int16)
         audio = audio.astype("float32")
@@ -411,7 +462,7 @@ class ASRExecutor(BaseExecutor):
 
         try:
             res = self(audio_file, model, lang, sample_rate, config, ckpt_path,
-                        decode_method, force_yes, device)
+                       decode_method, force_yes, device)
             logger.info('ASR Result: {}'.format(res))
             return True
         except Exception as e:
@@ -435,7 +486,8 @@ class ASRExecutor(BaseExecutor):
         audio_file = os.path.abspath(audio_file)
         self._check(audio_file, sample_rate, force_yes)
         paddle.set_device(device)
-        self._init_from_path(model, lang, sample_rate, config, decode_method, ckpt_path)
+        self._init_from_path(model, lang, sample_rate, config, decode_method,
+                             ckpt_path)
         self.preprocess(model, audio_file)
         self.infer(model)
         res = self.postprocess()  # Retrieve result of asr.