| | |
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| | | |
|---|
| | | # bias encoder |
|---|
| | | if self.bias_encoder_type == 'lstm': |
|---|
| | | logging.warning("enable bias encoder sampling and contextual training") |
|---|
| | | self.bias_encoder = torch.nn.LSTM(self.inner_dim, |
|---|
| | | self.inner_dim, |
|---|
| | | 2, |
|---|
| | |
|---|
| | | self.lstm_proj = None |
|---|
| | | self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim) |
|---|
| | | elif self.bias_encoder_type == 'mean': |
|---|
| | | logging.warning("enable bias encoder sampling and contextual training") |
|---|
| | | self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim) |
|---|
| | | else: |
|---|
| | | logging.error("Unsupport bias encoder type: {}".format(self.bias_encoder_type)) |
|---|
| | |
|---|
| | | |
|---|
| | | speech = speech.to(device=kwargs["device"]) |
|---|
| | | speech_lengths = speech_lengths.to(device=kwargs["device"]) |
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| | | |
|---|
| | | |
|---|
| | | # hotword |
|---|
| | | self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend) |
|---|
| | | |
|---|
| | |
|---|
| | | token, timestamp) |
|---|
| | | |
|---|
| | | result_i = {"key": key[i], "text": text_postprocessed, |
|---|
| | | "timestamp": time_stamp_postprocessed, |
|---|
| | | "timestamp": time_stamp_postprocessed, "raw_text": copy.copy(text_postprocessed) |
|---|
| | | } |
|---|
| | | |
|---|
| | | if ibest_writer is not None: |
|---|
| | | ibest_writer["token"][key[i]] = " ".join(token) |
|---|
| | | # ibest_writer["text"][key[i]] = text |
|---|
| | | # ibest_writer["raw_text"][key[i]] = text |
|---|
| | | ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed |
|---|
| | | ibest_writer["text"][key[i]] = text_postprocessed |
|---|
| | | else: |
|---|
