add the infer

paddlespeech/cli/s2t/infer.py

@@ -13,17 +13,24 @@
 # limitations under the License.
 import argparse
 import os
+import sys
 from typing import List
 from typing import Optional
 from typing import Union
 
+import soundfile
 import paddle
-
-from ..executor import BaseExecutor
-from ..utils import cli_register
-from ..utils import download_and_decompress
-from ..utils import logger
-from ..utils import MODEL_HOME
+from paddlespeech.cli.executor import BaseExecutor
+from paddlespeech.cli.utils import cli_register
+from paddlespeech.cli.utils import download_and_decompress
+from paddlespeech.cli.utils import logger
+from paddlespeech.cli.utils import MODEL_HOME
+from paddlespeech.s2t.exps.u2.config import get_cfg_defaults
+from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
+from paddlespeech.s2t.io.collator import SpeechCollator
+from paddlespeech.s2t.transform.transformation import Transformation
+from paddlespeech.s2t.utils.dynamic_import import dynamic_import
+from paddlespeech.s2t.utils.utility import UpdateConfig
 
 __all__ = ['S2TExecutor']
 
@@ -33,9 +40,44 @@ pretrained_models = {
         'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/conformer.model.tar.gz',
         'md5':
         '54e7a558a6e020c2f5fb224874943f97',
+        'cfg_path':
+        'conf/conformer.yaml',
+        'ckpt_path':
+        'exp/conformer/checkpoints/wenetspeech',
     }
 }
 
+model_alias = {
+    "ds2_offline": "paddlespeech.s2t.models.ds2:DeepSpeech2Model",
+    "ds2_online": "paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline",
+    "conformer": "paddlespeech.s2t.models.u2:U2Model",
+    "transformer": "paddlespeech.s2t.models.u2:U2Model",
+    "wenetspeech": "paddlespeech.s2t.models.u2:U2Model",
+}
+
+pretrain_model_alias = {
+    "ds2_online_zn": [
+        "https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz",
+        "", ""
+    ],
+    "ds2_offline_zn": [
+        "https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz",
+        "", ""
+    ],
+    "transformer_zn": [
+        "https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz",
+        "", ""
+    ],
+    "conformer_zn": [
+        "https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/conformer.model.tar.gz",
+        "", ""
+    ],
+    "wenetspeech_zn": [
+        "https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/conformer.model.tar.gz",
+        "conf/conformer.yaml", "exp/conformer/checkpoints/wenetspeech"
+    ],
+}
+
 
 @cli_register(
     name='paddlespeech.s2t', description='Speech to text infer command.')
@@ -63,7 +105,10 @@ class S2TExecutor(BaseExecutor):
             default=None,
             help='Checkpoint file of model.')
         self.parser.add_argument(
-            '--input', type=str, help='Audio file to recognize.')
+            '--input',
+            type=str,
+            default="../Downloads/asr-demo-1.wav",
+            help='Audio file to recognize.')
         self.parser.add_argument(
             '--device',
             type=str,
@@ -80,8 +125,10 @@ class S2TExecutor(BaseExecutor):
         res_path = os.path.join(MODEL_HOME, tag)
         decompressed_path = download_and_decompress(pretrained_models[tag],
                                                     res_path)
+        decompressed_path = os.path.abspath(decompressed_path)
         logger.info(
             'Use pretrained model stored in: {}'.format(decompressed_path))
+
         return decompressed_path
 
     def _init_from_path(self,
@@ -93,56 +140,194 @@ class S2TExecutor(BaseExecutor):
             Init model and other resources from a specific path.
         """
         if cfg_path is None or ckpt_path is None:
-            res_path = self._get_pretrained_path(
-                model_type + '_' + lang)  # wenetspeech_zh
-            cfg_path = os.path.join(res_path, 'conf/conformer.yaml')
-            ckpt_path = os.path.join(
-                res_path, 'exp/conformer/checkpoints/wenetspeech.pdparams')
+            tag = model_type + '_' + lang
+            res_path = self._get_pretrained_path(tag)  # wenetspeech_zh
+            self.cfg_path = os.path.join(res_path,
+                                         pretrained_models[tag]['cfg_path'])
+            self.ckpt_path = os.path.join(res_path,
+                                          pretrained_models[tag]['ckpt_path'])
             logger.info(res_path)
-            logger.info(cfg_path)
-            logger.info(ckpt_path)
+            logger.info(self.cfg_path)
+            logger.info(self.ckpt_path)
+        else:
+            self.cfg_path = os.path.abspath(cfg_path)
+            self.ckpt_path = os.path.abspath(ckpt_path)
+            res_path = os.path.dirname(
+                os.path.dirname(os.path.abspath(self.cfg_path)))
 
-        # Init body.
-        pass
+        os.chdir(res_path)
+        #Init body.
+        parser_args = self.parser_args
+        paddle.set_device(parser_args.device)
+        self.config = get_cfg_defaults()
+        self.config.merge_from_file(self.cfg_path)
+        self.config.decoding.decoding_method = "attention_rescoring"
+        #self.config.freeze()
+        model_conf = self.config.model
+        logger.info(model_conf)
+
+        with UpdateConfig(model_conf):
+            if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
+                self.config.collator.vocab_filepath = os.path.join(
+                    res_path, self.config.collator.vocab_filepath)
+                self.config.collator.vocab_filepath = os.path.join(
+                    res_path, self.config.collator.cmvn_path)
+                self.collate_fn_test = SpeechCollator.from_config(self.config)
+                model_conf.feat_size = self.collate_fn_test.feature_size
+                model_conf.dict_size = self.text_feature.vocab_size
+            elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
+                self.config.collator.vocab_filepath = os.path.join(
+                    res_path, self.config.collator.vocab_filepath)
+                self.text_feature = TextFeaturizer(
+                    unit_type=self.config.collator.unit_type,
+                    vocab_filepath=self.config.collator.vocab_filepath,
+                    spm_model_prefix=self.config.collator.spm_model_prefix)
+                model_conf.input_dim = self.config.collator.feat_dim
+                model_conf.output_dim = self.text_feature.vocab_size
+            else:
+                raise Exception("wrong type")
+        model_class = dynamic_import(parser_args.model, model_alias)
+        model = model_class.from_config(model_conf)
+        self.model = model
+        self.model.eval()
+
+        # load model
+        params_path = self.ckpt_path + ".pdparams"
+        model_dict = paddle.load(params_path)
+        self.model.set_state_dict(model_dict)
 
     def preprocess(self, input: Union[str, os.PathLike]):
         """
             Input preprocess and return paddle.Tensor stored in self.input.
             Input content can be a text(t2s), a file(s2t, cls) or a streaming(not supported yet).
         """
-        pass
+
+        parser_args = self.parser_args
+        config = self.config
+        audio_file = input
+        #print("audio_file", audio_file)
+        logger.info("audio_file"+ audio_file)
+
+        self.sr = config.collator.target_sample_rate
+
+        # Get the object for feature extraction
+        if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
+            audio, _ = collate_fn_test.process_utterance(
+                audio_file=audio_file, transcript=" ")
+            audio_len = audio.shape[0]
+            audio = paddle.to_tensor(audio, dtype='float32')
+            self.audio_len = paddle.to_tensor(audio_len)
+            self.audio = paddle.unsqueeze(audio, axis=0)
+            self.vocab_list = collate_fn_test.vocab_list
+            logger.info(f"audio feat shape: {self.audio.shape}")
+
+        elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
+            logger.info("get the preprocess conf")
+            preprocess_conf = os.path.join(
+                os.path.dirname(os.path.abspath(self.cfg_path)),
+                "preprocess.yaml")
+
+            cmvn_path: data / mean_std.json
+
+            logger.info(preprocess_conf)
+            preprocess_args = {"train": False}
+            preprocessing = Transformation(preprocess_conf)
+            audio, sample_rate = soundfile.read(
+                audio_file, dtype="int16", always_2d=True)
+            if sample_rate != self.sr:
+                logger.error(
+                    f"sample rate error: {sample_rate}, need {self.sr} ")
+                sys.exit(-1)
+            audio = audio[:, 0]
+            logger.info(f"audio shape: {audio.shape}")
+            # fbank
+            audio = preprocessing(audio, **preprocess_args)
+
+            self.audio_len = paddle.to_tensor(audio.shape[0])
+            self.audio = paddle.to_tensor(
+                audio, dtype='float32').unsqueeze(axis=0)
+            logger.info(f"audio feat shape: {self.audio.shape}")
+
+        else:
+            raise Exception("wrong type")
 
     @paddle.no_grad()
     def infer(self):
         """
             Model inference and result stored in self.output.
         """
+        cfg = self.config.decoding
+        parser_args = self.parser_args
+        audio = self.audio
+        audio_len = self.audio_len
+        if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
+            vocab_list = self.vocab_list
+            result_transcripts = self.model.decode(
+                audio,
+                audio_len,
+                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)
+            self.result_transcripts = result_transcripts[0]
+
+        elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
+            text_feature = self.text_feature
+            result_transcripts = self.model.decode(
+                audio,
+                audio_len,
+                text_feature=self.text_feature,
+                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,
+                ctc_weight=cfg.ctc_weight,
+                decoding_chunk_size=cfg.decoding_chunk_size,
+                num_decoding_left_chunks=cfg.num_decoding_left_chunks,
+                simulate_streaming=cfg.simulate_streaming)
+            self.result_transcripts = result_transcripts[0][0]
+        else:
+            raise Exception("invalid model name")
+
         pass
 
     def postprocess(self) -> Union[str, os.PathLike]:
         """
             Output postprocess and return human-readable results such as texts and audio files.
         """
-        pass
+        return self.result_transcripts
 
     def execute(self, argv: List[str]) -> bool:
-        parser_args = self.parser.parse_args(argv)
-        print(parser_args)
+        self.parser_args = self.parser.parse_args(argv)
 
-        model = parser_args.model
-        lang = parser_args.lang
-        config = parser_args.config
-        ckpt_path = parser_args.ckpt_path
-        audio_file = parser_args.input
-        device = parser_args.device
+        model = self.parser_args.model
+        lang = self.parser_args.lang
+        config = self.parser_args.config
+        ckpt_path = self.parser_args.ckpt_path
+        audio_file = os.path.abspath(self.parser_args.input)
+        device = self.parser_args.device
 
         try:
             self._init_from_path(model, lang, config, ckpt_path)
             self.preprocess(audio_file)
             self.infer()
             res = self.postprocess()  # Retrieve result of s2t.
-            print(res)
+            logger.info(res)
             return True
         except Exception as e:
             print(e)
             return False
+
+
+if __name__ == "__main__":
+    exe = S2TExecutor()
+    exe.execute('')