Author

游雁

2023-04-14 f0fdc051fbddc2a941b303730dba87df6658f9dd

Author

 funasr/export/models/CT_Transformer.py

@@ -10,7 +10,7 @@

class CT_Transformer(nn.Module):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
    https://arxiv.org/pdf/2003.01309.pdf
    """
@@ -81,7 +81,7 @@

class CT_Transformer_VadRealtime(nn.Module):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
    https://arxiv.org/pdf/2003.01309.pdf
    """

 funasr/export/models/e2e_asr_paraformer.py

@@ -19,7 +19,7 @@

class Paraformer(nn.Module):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
    https://arxiv.org/abs/2206.08317
    """
@@ -112,7 +112,7 @@

class BiCifParaformer(nn.Module):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
    https://arxiv.org/abs/2206.08317
    """

 funasr/models/decoder/contextual_decoder.py

@@ -102,7 +102,7 @@

class ContextualParaformerDecoder(ParaformerSANMDecoder):
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
    https://arxiv.org/abs/2006.01713
    """

 funasr/models/decoder/sanm_decoder.py

@@ -151,7 +151,7 @@

class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
    https://arxiv.org/abs/2006.01713

@@ -812,7 +812,7 @@

class ParaformerSANMDecoder(BaseTransformerDecoder):
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
    https://arxiv.org/abs/2006.01713
    """

 funasr/models/decoder/transformer_decoder.py

@@ -405,7 +405,7 @@

class ParaformerDecoderSAN(BaseTransformerDecoder):
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
    https://arxiv.org/abs/2006.01713
    """

 funasr/models/e2e_asr_paraformer.py

@@ -44,7 +44,7 @@

class Paraformer(AbsESPnetModel):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
    https://arxiv.org/abs/2206.08317
    """
@@ -612,7 +612,7 @@

class ParaformerBert(Paraformer):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Paraformer2: advanced paraformer with LFMMI and bert for non-autoregressive end-to-end speech recognition
    """


 funasr/models/e2e_tp.py

@@ -32,7 +32,7 @@

class TimestampPredictor(AbsESPnetModel):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    """

    def __init__(

 funasr/models/e2e_uni_asr.py

@@ -40,7 +40,7 @@

class UniASR(AbsESPnetModel):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    """

    def __init__(

 funasr/models/e2e_vad.py

@@ -35,6 +35,11 @@


class VADXOptions:
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """
    def __init__(
            self,
            sample_rate: int = 16000,
@@ -99,6 +104,11 @@


class E2EVadSpeechBufWithDoa(object):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """
    def __init__(self):
        self.start_ms = 0
        self.end_ms = 0
@@ -117,6 +127,11 @@


class E2EVadFrameProb(object):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """
    def __init__(self):
        self.noise_prob = 0.0
        self.speech_prob = 0.0
@@ -126,6 +141,11 @@


class WindowDetector(object):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """
    def __init__(self, window_size_ms: int, sil_to_speech_time: int,
                 speech_to_sil_time: int, frame_size_ms: int):
        self.window_size_ms = window_size_ms
@@ -192,6 +212,11 @@


class E2EVadModel(nn.Module):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """
    def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any], frontend=None):
        super(E2EVadModel, self).__init__()
        self.vad_opts = VADXOptions(**vad_post_args)

 funasr/models/encoder/opennmt_encoders/conv_encoder.py

@@ -67,7 +67,7 @@

class ConvEncoder(AbsEncoder):
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Convolution encoder in OpenNMT framework
    """


 funasr/models/encoder/opennmt_encoders/self_attention_encoder.py

@@ -117,7 +117,7 @@

class SelfAttentionEncoder(AbsEncoder):
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Self attention encoder in OpenNMT framework
    """


 funasr/models/encoder/sanm_encoder.py

@@ -117,7 +117,7 @@

class SANMEncoder(AbsEncoder):
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    San-m: Memory equipped self-attention for end-to-end speech recognition
    https://arxiv.org/abs/2006.01713

@@ -549,7 +549,7 @@

class SANMEncoderChunkOpt(AbsEncoder):
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
    https://arxiv.org/abs/2006.01713

@@ -962,7 +962,7 @@

class SANMVadEncoder(AbsEncoder):
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group

    """


 funasr/models/target_delay_transformer.py

@@ -14,7 +14,7 @@

class TargetDelayTransformer(AbsPunctuation):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
    https://arxiv.org/pdf/2003.01309.pdf
    """

 funasr/models/vad_realtime_transformer.py

@@ -12,7 +12,7 @@

class VadRealtimeTransformer(AbsPunctuation):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
    https://arxiv.org/pdf/2003.01309.pdf
    """

 funasr/modules/streaming_utils/chunk_utilis.py

@@ -11,7 +11,7 @@

class overlap_chunk():
    """
    author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    San-m: Memory equipped self-attention for end-to-end speech recognition
    https://arxiv.org/abs/2006.01713


 funasr/runtime/python/onnxruntime/demo_vad_offline.py

@@ -1,5 +1,5 @@
import soundfile
from funasr_onnx.vad_bin import Fsmn_vad
from funasr_onnx import Fsmn_vad


model_dir = "/mnt/ailsa.zly/tfbase/espnet_work/FunASR_dev_zly/export/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"

 funasr/runtime/python/onnxruntime/demo_vad_online.py

@@ -1,10 +1,10 @@
import soundfile
from funasr_onnx.vad_online_bin import Fsmn_vad
from funasr_onnx import Fsmn_vad_online


model_dir = "/mnt/ailsa.zly/tfbase/espnet_work/FunASR_dev_zly/export/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
wav_path = "/mnt/ailsa.zly/tfbase/espnet_work/FunASR_dev_zly/egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/vad_example_16k.wav"
model = Fsmn_vad(model_dir)
model = Fsmn_vad_online(model_dir)


##online vad

 funasr/runtime/python/onnxruntime/funasr_onnx/__init__.py

@@ -1,5 +1,6 @@
# -*- encoding: utf-8 -*-
from .paraformer_bin import Paraformer
from .vad_bin import Fsmn_vad
from .vad_bin import Fsmn_vad_online
from .punc_bin import CT_Transformer
from .punc_bin import CT_Transformer_VadRealtime

 funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py

@@ -14,7 +14,7 @@

class CT_Transformer():
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
    https://arxiv.org/pdf/2003.01309.pdf
    """
@@ -125,7 +125,7 @@

class CT_Transformer_VadRealtime(CT_Transformer):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
    https://arxiv.org/pdf/2003.01309.pdf
    """

 funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py

@@ -11,13 +11,18 @@
from .utils.utils import (ONNXRuntimeError,
                          OrtInferSession, get_logger,
                          read_yaml)
from .utils.frontend import WavFrontend
from .utils.frontend import WavFrontend, WavFrontendOnline
from .utils.e2e_vad import E2EVadModel

logging = get_logger()


class Fsmn_vad():
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """
    def __init__(self, model_dir: Union[str, Path] = None,
                 batch_size: int = 1,
                 device_id: Union[str, int] = "-1",
@@ -151,4 +156,125 @@
        outputs = self.ort_infer(feats)
        scores, out_caches = outputs[0], outputs[1:]
        return scores, out_caches


class Fsmn_vad_online():
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """
    def __init__(self, model_dir: Union[str, Path] = None,
                 batch_size: int = 1,
                 device_id: Union[str, int] = "-1",
                 quantize: bool = False,
                 intra_op_num_threads: int = 4,
                 max_end_sil: int = None,
                 ):
		
        if not Path(model_dir).exists():
            raise FileNotFoundError(f'{model_dir} does not exist.')
		
        model_file = os.path.join(model_dir, 'model.onnx')
        if quantize:
            model_file = os.path.join(model_dir, 'model_quant.onnx')
        config_file = os.path.join(model_dir, 'vad.yaml')
        cmvn_file = os.path.join(model_dir, 'vad.mvn')
        config = read_yaml(config_file)
		
        self.frontend = WavFrontendOnline(
            cmvn_file=cmvn_file,
            **config['frontend_conf']
        )
        self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
        self.batch_size = batch_size
        self.vad_scorer = E2EVadModel(config["vad_post_conf"])
        self.max_end_sil = max_end_sil if max_end_sil is not None else config["vad_post_conf"]["max_end_silence_time"]
        self.encoder_conf = config["encoder_conf"]
    
    def prepare_cache(self, in_cache: list = []):
        if len(in_cache) > 0:
            return in_cache
        fsmn_layers = self.encoder_conf["fsmn_layers"]
        proj_dim = self.encoder_conf["proj_dim"]
        lorder = self.encoder_conf["lorder"]
        for i in range(fsmn_layers):
            cache = np.zeros((1, proj_dim, lorder - 1, 1)).astype(np.float32)
            in_cache.append(cache)
        return in_cache
	
    def __call__(self, audio_in: np.ndarray, **kwargs) -> List:
        waveforms = np.expand_dims(audio_in, axis=0)
		
        param_dict = kwargs.get('param_dict', dict())
        is_final = param_dict.get('is_final', False)
        feats, feats_len = self.extract_feat(waveforms, is_final)
        segments = []
        if feats.size != 0:
            in_cache = param_dict.get('in_cache', list())
            in_cache = self.prepare_cache(in_cache)
            try:
                inputs = [feats]
                inputs.extend(in_cache)
                scores, out_caches = self.infer(inputs)
                param_dict['in_cache'] = out_caches
                waveforms = self.frontend.get_waveforms()
                segments = self.vad_scorer(scores, waveforms, is_final=is_final, max_end_sil=self.max_end_sil,
                                           online=True)
			
			
            except ONNXRuntimeError:
                # logging.warning(traceback.format_exc())
                logging.warning("input wav is silence or noise")
                segments = []
        return segments
	
    def load_data(self,
                  wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
        def load_wav(path: str) -> np.ndarray:
            waveform, _ = librosa.load(path, sr=fs)
            return waveform
		
        if isinstance(wav_content, np.ndarray):
            return [wav_content]
		
        if isinstance(wav_content, str):
            return [load_wav(wav_content)]
		
        if isinstance(wav_content, list):
            return [load_wav(path) for path in wav_content]
		
        raise TypeError(
            f'The type of {wav_content} is not in [str, np.ndarray, list]')
	
    def extract_feat(self,
                     waveforms: np.ndarray, is_final: bool = False
                     ) -> Tuple[np.ndarray, np.ndarray]:
        waveforms_lens = np.zeros(waveforms.shape[0]).astype(np.int32)
        for idx, waveform in enumerate(waveforms):
            waveforms_lens[idx] = waveform.shape[-1]
		
        feats, feats_len = self.frontend.extract_fbank(waveforms, waveforms_lens, is_final)
        # feats.append(feat)
        # feats_len.append(feat_len)
		
        # feats = self.pad_feats(feats, np.max(feats_len))
        # feats_len = np.array(feats_len).astype(np.int32)
        return feats.astype(np.float32), feats_len.astype(np.int32)
	
    @staticmethod
    def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
        def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
            pad_width = ((0, max_feat_len - cur_len), (0, 0))
            return np.pad(feat, pad_width, 'constant', constant_values=0)
		
        feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
        feats = np.array(feat_res).astype(np.float32)
        return feats
	
    def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]:
		
        outputs = self.ort_infer(feats)
        scores, out_caches = outputs[0], outputs[1:]
        return scores, out_caches


 funasr/runtime/python/onnxruntime/funasr_onnx/vad_online_bin.py

File was deleted

 funasr/runtime/python/onnxruntime/setup.py

@@ -13,7 +13,7 @@


MODULE_NAME = 'funasr_onnx'
VERSION_NUM = '0.0.3'
VERSION_NUM = '0.0.4'

setuptools.setup(
    name=MODULE_NAME,