| | |
|---|
| | | if quantize: |
|---|
| | | model_file = os.path.join(model_dir, "model_quant.onnx") |
|---|
| | | if not os.path.exists(model_file): |
|---|
| | | print(".onnx is not exist, begin to export onnx") |
|---|
| | | print(".onnx does not exist, begin to export onnx") |
|---|
| | | try: |
|---|
| | | from funasr import AutoModel |
|---|
| | | except: |
|---|
| | |
|---|
| | | model_eb_file = os.path.join(model_dir, "model_eb.onnx") |
|---|
| | | |
|---|
| | | if not (os.path.exists(model_eb_file) and os.path.exists(model_bb_file)): |
|---|
| | | print(".onnx is not exist, begin to export onnx") |
|---|
| | | print(".onnx does not exist, begin to export onnx") |
|---|
| | | try: |
|---|
| | | from funasr import AutoModel |
|---|
| | | except: |
|---|
| | |
|---|
| | | self.pred_bias = config["model_conf"]["predictor_bias"] |
|---|
| | | else: |
|---|
| | | self.pred_bias = 0 |
|---|
| | | if "lang" in config: |
|---|
| | | self.language = config["lang"] |
|---|
| | | else: |
|---|
| | | self.language = None |
|---|
| | | |
|---|
| | | def __call__( |
|---|
| | | self, wav_content: Union[str, np.ndarray, List[str]], hotwords: str, **kwargs |
|---|
| | |
|---|
| | | # ) -> List: |
|---|
| | | # make hotword list |
|---|
| | | hotwords, hotwords_length = self.proc_hotword(hotwords) |
|---|
| | | # import pdb; pdb.set_trace() |
|---|
| | | [bias_embed] = self.eb_infer(hotwords, hotwords_length) |
|---|
| | | # index from bias_embed |
|---|
| | | bias_embed = bias_embed.transpose(1, 0, 2) |
|---|
| | |
|---|
| | | return np.array(hotwords) |
|---|
| | | |
|---|
| | | hotword_int = [word_map(i) for i in hotwords] |
|---|
| | | # import pdb; pdb.set_trace() |
|---|
| | | |
|---|
| | | hotword_int.append(np.array([1])) |
|---|
| | | hotwords = pad_list(hotword_int, pad_value=0, max_len=10) |
|---|
| | | # import pdb; pdb.set_trace() |
|---|
| | | |
|---|
| | | return hotwords, hotwords_length |
|---|
| | | |
|---|
| | | def bb_infer( |
|---|
