[to #42322933] Fix ASR error when resample failed, and add all asr models UT, add apply-cmvn for pytorch models

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10465241

data/test/audios/asr_example_8K.wav

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data/test/audios/asr_example_cn_dialect.wav

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data/test/audios/asr_example_cn_en.wav

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data/test/audios/asr_example_en.wav

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data/test/audios/asr_example_es.wav

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data/test/audios/asr_example_id.wav

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+size 112078

data/test/audios/asr_example_ja.wav

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+size 200556

data/test/audios/asr_example_ko.wav

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+size 162636

data/test/audios/asr_example_ru.wav

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+size 160204

modelscope/pipelines/audio/asr_inference_pipeline.py

@@ -47,22 +47,28 @@ class AutomaticSpeechRecognitionPipeline(Pipeline):
 
         if isinstance(audio_in, str):
             # load pcm data from url if audio_in is url str
-            self.audio_in = load_bytes_from_url(audio_in)
+            self.audio_in, checking_audio_fs = load_bytes_from_url(audio_in)
         elif isinstance(audio_in, bytes):
             # load pcm data from wav data if audio_in is wave format
-            self.audio_in = extract_pcm_from_wav(audio_in)
+            self.audio_in, checking_audio_fs = extract_pcm_from_wav(audio_in)
         else:
             self.audio_in = audio_in
 
+        # set the sample_rate of audio_in if checking_audio_fs is valid
+        if checking_audio_fs is not None:
+            self.audio_fs = checking_audio_fs
+
         if recog_type is None or audio_format is None:
             self.recog_type, self.audio_format, self.audio_in = asr_utils.type_checking(
                 audio_in=self.audio_in,
                 recog_type=recog_type,
                 audio_format=audio_format)
 
-        if hasattr(asr_utils, 'sample_rate_checking') and audio_fs is None:
-            self.audio_fs = asr_utils.sample_rate_checking(
+        if hasattr(asr_utils, 'sample_rate_checking'):
+            checking_audio_fs = asr_utils.sample_rate_checking(
                 self.audio_in, self.audio_format)
+            if checking_audio_fs is not None:
+                self.audio_fs = checking_audio_fs
 
         if self.preprocessor is None:
             self.preprocessor = WavToScp()
@@ -80,7 +86,7 @@ class AutomaticSpeechRecognitionPipeline(Pipeline):
 
         logger.info(f"Decoding with {inputs['audio_format']} files ...")
 
-        data_cmd: Sequence[Tuple[str, str]]
+        data_cmd: Sequence[Tuple[str, str, str]]
         if inputs['audio_format'] == 'wav' or inputs['audio_format'] == 'pcm':
             data_cmd = ['speech', 'sound']
         elif inputs['audio_format'] == 'kaldi_ark':
@@ -88,6 +94,9 @@ class AutomaticSpeechRecognitionPipeline(Pipeline):
         elif inputs['audio_format'] == 'tfrecord':
             data_cmd = ['speech', 'tfrecord']
 
+        if inputs.__contains__('mvn_file'):
+            data_cmd.append(inputs['mvn_file'])
+
         # generate asr inference command
         cmd = {
             'model_type': inputs['model_type'],

modelscope/pipelines/audio/kws_kwsbp_pipeline.py

@@ -51,10 +51,10 @@ class KeyWordSpottingKwsbpPipeline(Pipeline):
 
         if isinstance(audio_in, str):
             # load pcm data from url if audio_in is url str
-            audio_in = load_bytes_from_url(audio_in)
+            audio_in, audio_fs = load_bytes_from_url(audio_in)
         elif isinstance(audio_in, bytes):
             # load pcm data from wav data if audio_in is wave format
-            audio_in = extract_pcm_from_wav(audio_in)
+            audio_in, audio_fs = extract_pcm_from_wav(audio_in)
 
         output = self.preprocessor.forward(self.model.forward(), audio_in)
         output = self.forward(output)

modelscope/preprocessors/asr.py

@@ -133,6 +133,12 @@ class WavToScp(Preprocessor):
             else:
                 inputs['asr_model_config'] = asr_model_config
 
+            if inputs['model_config'].__contains__('mvn_file'):
+                mvn_file = os.path.join(inputs['model_workspace'],
+                                        inputs['model_config']['mvn_file'])
+                assert os.path.exists(mvn_file), 'mvn_file does not exist'
+                inputs['mvn_file'] = mvn_file
+
         elif inputs['model_type'] == Frameworks.tf:
             assert inputs['model_config'].__contains__(
                 'vocab_file'), 'vocab_file does not exist'

modelscope/utils/audio/audio_utils.py

@@ -57,6 +57,7 @@ def update_conf(origin_config_file, new_config_file, conf_item: [str, str]):
 
 def extract_pcm_from_wav(wav: bytes) -> bytes:
     data = wav
+    sample_rate = None
     if len(data) > 44:
         frame_len = 44
         file_len = len(data)
@@ -70,29 +71,33 @@ def extract_pcm_from_wav(wav: bytes) -> bytes:
                         'Subchunk1ID'] == 'fmt ':
                 header_fields['SubChunk1Size'] = struct.unpack(
                     '<I', data[16:20])[0]
+                header_fields['SampleRate'] = struct.unpack('<I',
+                                                            data[24:28])[0]
+                sample_rate = header_fields['SampleRate']
 
                 if header_fields['SubChunk1Size'] == 16:
                     frame_len = 44
                 elif header_fields['SubChunk1Size'] == 18:
                     frame_len = 46
                 else:
-                    return data
+                    return data, sample_rate
 
                 data = wav[frame_len:file_len]
         except Exception:
             # no treatment
             pass
 
-    return data
+    return data, sample_rate
 
 
 def load_bytes_from_url(url: str) -> Union[bytes, str]:
+    sample_rate = None
     result = urlparse(url)
     if result.scheme is not None and len(result.scheme) > 0:
         storage = HTTPStorage()
         data = storage.read(url)
-        data = extract_pcm_from_wav(data)
+        data, sample_rate = extract_pcm_from_wav(data)
     else:
         data = url
 
-    return data
+    return data, sample_rate

tests/pipelines/test_automatic_speech_recognition.py

@@ -45,6 +45,10 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase,
             'checking_item': OutputKeys.TEXT,
             'example': 'wav_example'
         },
+        'test_run_with_url_pytorch': {
+            'checking_item': OutputKeys.TEXT,
+            'example': 'wav_example'
+        },
         'test_run_with_url_tf': {
             'checking_item': OutputKeys.TEXT,
             'example': 'wav_example'
@@ -74,6 +78,170 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase,
         }
     }
 
+    all_models_info = [
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1',
+            'wav_path': 'data/test/audios/asr_example.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id': 'speech_paraformer_asr_nat-aishell1-pytorch',
+            'wav_path': 'data/test/audios/asr_example.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1',
+            'wav_path': 'data/test/audios/asr_example.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1',
+            'wav_path': 'data/test/audios/asr_example_8K.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_8K.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example_8K.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-cn-en-moe-16k-vocab8358-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_cn_en.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-cn-en-moe-16k-vocab8358-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example_cn_en.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_cn_dialect.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example_cn_dialect.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_paraformer_asr_nat-zh-cn-16k-common-vocab3444-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_paraformer_asr_nat-zh-cn-8k-common-vocab3444-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_8K.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example_en.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_en.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example_ru.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_ru.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example_es.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_es.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example_ko.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_ko.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_ja.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example_ja.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-online',
+            'wav_path': 'data/test/audios/asr_example_id.wav'
+        },
+        {
+            'model_group': 'damo',
+            'model_id':
+            'speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-offline',
+            'wav_path': 'data/test/audios/asr_example_id.wav'
+        },
+    ]
+
     def setUp(self) -> None:
         self.am_pytorch_model_id = 'damo/speech_paraformer_asr_nat-aishell1-pytorch'
         self.am_tf_model_id = 'damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1'
@@ -90,7 +258,7 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase,
     def run_pipeline(self,
                      model_id: str,
                      audio_in: Union[str, bytes],
-                     sr: int = 16000) -> Dict[str, Any]:
+                     sr: int = None) -> Dict[str, Any]:
         inference_16k_pipline = pipeline(
             task=Tasks.auto_speech_recognition, model=model_id)
 
@@ -136,46 +304,7 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase,
         return audio, fs
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
-    def test_run_with_wav_pytorch(self):
-        """run with single waveform file
-        """
-
-        logger.info('Run ASR test with waveform file (pytorch)...')
-
-        wav_file_path = os.path.join(os.getcwd(), WAV_FILE)
-
-        rec_result = self.run_pipeline(
-            model_id=self.am_pytorch_model_id, audio_in=wav_file_path)
-        self.check_result('test_run_with_wav_pytorch', rec_result)
-
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
-    def test_run_with_pcm_pytorch(self):
-        """run with wav data
-        """
-
-        logger.info('Run ASR test with wav data (pytorch)...')
-
-        audio, sr = self.wav2bytes(os.path.join(os.getcwd(), WAV_FILE))
-
-        rec_result = self.run_pipeline(
-            model_id=self.am_pytorch_model_id, audio_in=audio, sr=sr)
-        self.check_result('test_run_with_pcm_pytorch', rec_result)
-
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
-    def test_run_with_wav_tf(self):
-        """run with single waveform file
-        """
-
-        logger.info('Run ASR test with waveform file (tensorflow)...')
-
-        wav_file_path = os.path.join(os.getcwd(), WAV_FILE)
-
-        rec_result = self.run_pipeline(
-            model_id=self.am_tf_model_id, audio_in=wav_file_path)
-        self.check_result('test_run_with_wav_tf', rec_result)
-
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
-    def test_run_with_pcm_tf(self):
+    def test_run_with_pcm(self):
         """run with wav data
         """
 
@@ -187,8 +316,33 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase,
             model_id=self.am_tf_model_id, audio_in=audio, sr=sr)
         self.check_result('test_run_with_pcm_tf', rec_result)
 
+        logger.info('Run ASR test with wav data (pytorch)...')
+
+        rec_result = self.run_pipeline(
+            model_id=self.am_pytorch_model_id, audio_in=audio, sr=sr)
+        self.check_result('test_run_with_pcm_pytorch', rec_result)
+
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
-    def test_run_with_url_tf(self):
+    def test_run_with_wav(self):
+        """run with single waveform file
+        """
+
+        logger.info('Run ASR test with waveform file (tensorflow)...')
+
+        wav_file_path = os.path.join(os.getcwd(), WAV_FILE)
+
+        rec_result = self.run_pipeline(
+            model_id=self.am_tf_model_id, audio_in=wav_file_path)
+        self.check_result('test_run_with_wav_tf', rec_result)
+
+        logger.info('Run ASR test with waveform file (pytorch)...')
+
+        rec_result = self.run_pipeline(
+            model_id=self.am_pytorch_model_id, audio_in=wav_file_path)
+        self.check_result('test_run_with_wav_pytorch', rec_result)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_with_url(self):
         """run with single url file
         """
 
@@ -198,6 +352,12 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase,
             model_id=self.am_tf_model_id, audio_in=URL_FILE)
         self.check_result('test_run_with_url_tf', rec_result)
 
+        logger.info('Run ASR test with url file (pytorch)...')
+
+        rec_result = self.run_pipeline(
+            model_id=self.am_pytorch_model_id, audio_in=URL_FILE)
+        self.check_result('test_run_with_url_pytorch', rec_result)
+
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_run_with_wav_dataset_pytorch(self):
         """run with datasets, and audio format is waveform
@@ -217,7 +377,6 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase,
                  data.text  # hypothesis text
         """
 
-        logger.info('Run ASR test with waveform dataset (pytorch)...')
         logger.info('Downloading waveform testsets file ...')
 
         dataset_path = download_and_untar(
@@ -225,40 +384,38 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase,
             LITTLE_TESTSETS_URL, self.workspace)
         dataset_path = os.path.join(dataset_path, 'wav', 'test')
 
+        logger.info('Run ASR test with waveform dataset (tensorflow)...')
+
+        rec_result = self.run_pipeline(
+            model_id=self.am_tf_model_id, audio_in=dataset_path)
+        self.check_result('test_run_with_wav_dataset_tf', rec_result)
+
+        logger.info('Run ASR test with waveform dataset (pytorch)...')
+
         rec_result = self.run_pipeline(
             model_id=self.am_pytorch_model_id, audio_in=dataset_path)
         self.check_result('test_run_with_wav_dataset_pytorch', rec_result)
 
-    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
-    def test_run_with_wav_dataset_tf(self):
-        """run with datasets, and audio format is waveform
-           datasets directory:
-             <dataset_path>
-               wav
-                 test   # testsets
-                   xx.wav
-                   ...
-                 dev    # devsets
-                   yy.wav
-                   ...
-                 train  # trainsets
-                   zz.wav
-                   ...
-               transcript
-                 data.text  # hypothesis text
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    def test_run_with_all_models(self):
+        """run with all models
         """
 
-        logger.info('Run ASR test with waveform dataset (tensorflow)...')
-        logger.info('Downloading waveform testsets file ...')
+        logger.info('Run ASR test with all models')
 
-        dataset_path = download_and_untar(
-            os.path.join(self.workspace, LITTLE_TESTSETS_FILE),
-            LITTLE_TESTSETS_URL, self.workspace)
-        dataset_path = os.path.join(dataset_path, 'wav', 'test')
-
-        rec_result = self.run_pipeline(
-            model_id=self.am_tf_model_id, audio_in=dataset_path)
-        self.check_result('test_run_with_wav_dataset_tf', rec_result)
+        for item in self.all_models_info:
+            model_id = item['model_group'] + '/' + item['model_id']
+            wav_path = item['wav_path']
+            rec_result = self.run_pipeline(
+                model_id=model_id, audio_in=wav_path)
+            if rec_result.__contains__(OutputKeys.TEXT):
+                logger.info(ColorCodes.MAGENTA + str(item['model_id']) + ' '
+                            + ColorCodes.YELLOW
+                            + str(rec_result[OutputKeys.TEXT])
+                            + ColorCodes.END)
+            else:
+                logger.info(ColorCodes.MAGENTA + str(rec_result)
+                            + ColorCodes.END)
 
     @unittest.skip('demo compatibility test is only enabled on a needed-basis')
     def test_demo_compatibility(self):