Merge pull request #2 from Jackwaterveg/cli_infer

LGTM

paddlespeech/cli/asr/infer.py

@@ -18,17 +18,17 @@ from typing import List
 from typing import Optional
 from typing import Union
 
+import librosa
 import paddle
 import soundfile
+from yacs.config import CfgNode
 
 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.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
@@ -36,7 +36,7 @@ from paddlespeech.s2t.utils.utility import UpdateConfig
 __all__ = ['ASRExecutor']
 
 pretrained_models = {
-    "wenetspeech_zh": {
+    "wenetspeech_zh_16k": {
         'url':
         'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/conformer.model.tar.gz',
         'md5':
@@ -73,7 +73,15 @@ class ASRExecutor(BaseExecutor):
             default='wenetspeech',
             help='Choose model type of asr task.')
         self.parser.add_argument(
-            '--lang', type=str, default='zh', help='Choose model language.')
+            '--lang',
+            type=str,
+            default='zh',
+            help='Choose model language. zh or en')
+        self.parser.add_argument(
+            "--model_sample_rate",
+            type=int,
+            default=16000,
+            help='Choose the audio sample rate of the model. 8000 or 16000')
         self.parser.add_argument(
             '--config',
             type=str,
@@ -109,13 +117,16 @@ class ASRExecutor(BaseExecutor):
     def _init_from_path(self,
                         model_type: str='wenetspeech',
                         lang: str='zh',
+                        model_sample_rate: int=16000,
                         cfg_path: Optional[os.PathLike]=None,
-                        ckpt_path: Optional[os.PathLike]=None):
+                        ckpt_path: Optional[os.PathLike]=None,
+                        device: str='cpu'):
         """
             Init model and other resources from a specific path.
         """
         if cfg_path is None or ckpt_path is None:
-            tag = model_type + '_' + lang
+            model_sample_rate_str = '16k' if model_sample_rate == 16000 else '8k'
+            tag = model_type + '_' + lang + '_' + model_sample_rate_str
             res_path = self._get_pretrained_path(tag)  # wenetspeech_zh
             self.cfg_path = os.path.join(res_path,
                                          pretrained_models[tag]['cfg_path'])
@@ -130,40 +141,44 @@ class ASRExecutor(BaseExecutor):
             res_path = os.path.dirname(
                 os.path.dirname(os.path.abspath(self.cfg_path)))
 
-        # Enter the path of model root
-        os.chdir(res_path)
-
         #Init body.
-        parser_args = self.parser_args
-        paddle.set_device(parser_args.device)
-        self.config = get_cfg_defaults()
+        paddle.set_device(device)
+        self.config = CfgNode(new_allowed=True)
         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":
+            if model_type == "ds2_online" or model_type == "ds2_offline":
+                from paddlespeech.s2t.io.collator import SpeechCollator
                 self.config.collator.vocab_filepath = os.path.join(
                     res_path, self.config.collator.vocab_filepath)
-                self.config.collator.vocab_filepath = os.path.join(
+                self.config.collator.mean_std_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":
+                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.collate_fn_test.feature_size
+                model_conf.output_dim = text_feature.vocab_size
+            elif model_type == "conformer" or model_type == "transformer" or model_type == "wenetspeech":
                 self.config.collator.vocab_filepath = os.path.join(
                     res_path, self.config.collator.vocab_filepath)
-                self.text_feature = TextFeaturizer(
+                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
+                model_conf.output_dim = text_feature.vocab_size
             else:
                 raise Exception("wrong type")
-        model_class = dynamic_import(parser_args.model, model_alias)
+        self.config.freeze()
+        # Enter the path of model root
+        os.chdir(res_path)
+
+        model_class = dynamic_import(model_type, model_alias)
         model = model_class.from_config(model_conf)
         self.model = model
         self.model.eval()
@@ -173,75 +188,94 @@ class ASRExecutor(BaseExecutor):
         model_dict = paddle.load(params_path)
         self.model.set_state_dict(model_dict)
 
-    def preprocess(self, input: Union[str, os.PathLike]):
+    def preprocess(self, model_type: str, input: Union[str, os.PathLike]):
         """
             Input preprocess and return paddle.Tensor stored in self.input.
             Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
         """
 
-        parser_args = self.parser_args
-        config = self.config
         audio_file = input
-        logger.info("audio_file" + audio_file)
+        logger.info("Preprocess audio_file:" + audio_file)
 
-        self.sr = config.collator.target_sample_rate
+        config_target_sample_rate = self.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(
+        if model_type == "ds2_online" or model_type == "ds2_offline":
+            audio, _ = self.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":
+            audio_len = paddle.to_tensor(audio_len)
+            audio = paddle.unsqueeze(audio, axis=0)
+            vocab_list = collate_fn_test.vocab_list
+            self._inputs["audio"] = audio
+            self._inputs["audio_len"] = audio_len
+            logger.info(f"audio feat shape: {audio.shape}")
+
+        elif model_type == "conformer" or model_type == "transformer" or model_type == "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)
+            logger.info("read the audio file")
             audio, sample_rate = soundfile.read(
                 audio_file, dtype="int16", always_2d=True)
-            if sample_rate != self.sr:
+
+            if self.change_format:
+                if audio.shape[1] >= 2:
+                    audio = audio.mean(axis=1)
+                else:
+                    audio = audio[:, 0]
+                audio = audio.astype("float32")
+                audio = librosa.resample(audio, sample_rate,
+                                         self.target_sample_rate)
+                sample_rate = self.target_sample_rate
+                audio = audio.astype("int16")
+            else:
+                audio = audio[:, 0]
+
+            if sample_rate != config_target_sample_rate:
                 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}")
+            audio_len = paddle.to_tensor(audio.shape[0])
+            audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
+            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)
+            self._inputs["audio"] = audio
+            self._inputs["audio_len"] = audio_len
+            logger.info(f"audio feat shape: {audio.shape}")
 
         else:
             raise Exception("wrong type")
 
     @paddle.no_grad()
-    def infer(self):
+    def infer(self, model_type: str):
         """
             Model inference and result stored in self.output.
         """
+        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)
         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
+        audio = self._inputs["audio"]
+        audio_len = self._inputs["audio_len"]
+        if model_type == "ds2_online" or model_type == "ds2_offline":
             result_transcripts = self.model.decode(
                 audio,
                 audio_len,
-                vocab_list,
+                text_feature.vocab_list,
                 decoding_method=cfg.decoding_method,
                 lang_model_path=cfg.lang_model_path,
                 beam_alpha=cfg.alpha,
@@ -250,14 +284,13 @@ class ASRExecutor(BaseExecutor):
                 cutoff_prob=cfg.cutoff_prob,
                 cutoff_top_n=cfg.cutoff_top_n,
                 num_processes=cfg.num_proc_bsearch)
-            self.result_transcripts = result_transcripts[0]
+            self._outputs["result"] = result_transcripts[0]
 
-        elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
-            text_feature = self.text_feature
+        elif model_type == "conformer" or model_type == "transformer" or model_type == "wenetspeech":
             result_transcripts = self.model.decode(
                 audio,
                 audio_len,
-                text_feature=self.text_feature,
+                text_feature=text_feature,
                 decoding_method=cfg.decoding_method,
                 lang_model_path=cfg.lang_model_path,
                 beam_alpha=cfg.alpha,
@@ -270,46 +303,110 @@ class ASRExecutor(BaseExecutor):
                 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]
+            self._outputs["result"] = 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.
         """
-        return self.result_transcripts
+        return self._outputs["result"]
+
+    def _check(self, audio_file: str, model_sample_rate: int):
+        self.target_sample_rate = model_sample_rate
+        if self.target_sample_rate != 16000 and self.target_sample_rate != 8000:
+            logger.error(
+                "please input --model_sample_rate 8000 or --model_sample_rate 16000"
+            )
+            raise Exception("invalid sample rate")
+            sys.exit(-1)
+
+        if not os.path.isfile(audio_file):
+            logger.error("Please input the right audio file path")
+            sys.exit(-1)
+
+        logger.info("checking the audio file format......")
+        try:
+            sig, sample_rate = soundfile.read(
+                audio_file, dtype="int16", always_2d=True)
+        except Exception as e:
+            logger.error(str(e))
+            logger.error(
+                "can not open the audio file, please check the audio file format is 'wav'. \n \
+                 you can try to use sox to change the file format.\n \
+                 For example: \n \
+                 sample rate: 16k \n \
+                 sox input_audio.xx --rate 16k --bits 16 --channels 1 output_audio.wav \n \
+                 sample rate: 8k \n \
+                 sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \
+                 ")
+            sys.exit(-1)
+        logger.info("The sample rate is %d" % sample_rate)
+        if sample_rate != self.target_sample_rate:
+            logger.warning(
+                "The sample rate of the input file is not {}.\n \
+                            The program will resample the wav file to {}.\n \
+                            If the result does not meet your expectations，\n \
+                            Please input the 16k 16bit 1 channel wav file. \
+                        "
+                .format(self.target_sample_rate, self.target_sample_rate))
+            while (True):
+                logger.info(
+                    "Whether to change the sample rate and the channel. Y: change the sample. N: exit the prgream."
+                )
+                content = input("Input(Y/N):")
+                if content.strip() == "Y" or content.strip(
+                ) == "y" or content.strip() == "yes" or content.strip() == "Yes":
+                    logger.info(
+                        "change the sampele rate, channel to 16k and 1 channel")
+                    break
+                elif content.strip() == "N" or content.strip(
+                ) == "n" or content.strip() == "no" or content.strip() == "No":
+                    logger.info("Exit the program")
+                    exit(1)
+                else:
+                    logger.warning("Not regular input, please input again")
+
+            self.change_format = True
+        else:
+            logger.info("The audio file format is right")
+            self.change_format = False
 
     def execute(self, argv: List[str]) -> bool:
         """
             Command line entry.
         """
-        self.parser_args = self.parser.parse_args(argv)
+        parser_args = self.parser.parse_args(argv)
 
-        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
+        model = parser_args.model
+        lang = parser_args.lang
+        model_sample_rate = parser_args.model_sample_rate
+        config = parser_args.config
+        ckpt_path = parser_args.ckpt_path
+        audio_file = parser_args.input
+        device = parser_args.device
 
         try:
-            res = self(model, lang, config, ckpt_path, audio_file, device)
+            res = self(model, lang, model_sample_rate, config, ckpt_path,
+                       audio_file, device)
             logger.info('ASR Result: {}'.format(res))
             return True
         except Exception as e:
             print(e)
             return False
 
-    def __call__(self, model, lang, config, ckpt_path, audio_file, device):
+    def __call__(self, model, lang, model_sample_rate, config, ckpt_path,
+                 audio_file, device):
         """
             Python API to call an executor.
         """
-        self._init_from_path(model, lang, config, ckpt_path)
-        self.preprocess(audio_file)
-        self.infer()
+        audio_file = os.path.abspath(audio_file)
+        self._check(audio_file, model_sample_rate)
+        self._init_from_path(model, lang, model_sample_rate, config, ckpt_path,
+                             device)
+        self.preprocess(model, audio_file)
+        self.infer(model)
         res = self.postprocess()  # Retrieve result of asr.
 
         return res