update egs_modelscope paraformer-large-en

北念

2023-10-17 fde48a865253b21f874dedf384c1bd8b59481112

update egs_modelscope paraformer-large-en

 egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/infer.py

@@ -16,7 +16,7 @@

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--model', type=str, default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
    parser.add_argument('--model', type=str, default="damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020")
    parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
    parser.add_argument('--output_dir', type=str, default="./results/")
    parser.add_argument('--decoding_mode', type=str, default="normal")

 egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/infer.sh

@@ -6,7 +6,7 @@

stage=1
stop_stage=2
model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model="damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020"
data_dir="./data/test"
output_dir="./results"
batch_size=64

 funasr/bin/asr_infer.py

@@ -1918,6 +1918,8 @@
            nbest: int = 1,
            streaming: bool = False,
            frontend_conf: dict = None,
            language: str = None,
            task: str = "transcribe",
            **kwargs,
    ):

@@ -1960,6 +1962,8 @@
        self.device = device
        self.dtype = dtype
        self.frontend = frontend
        self.language = language
        self.task = task

    @torch.no_grad()
    def __call__(
@@ -1986,10 +1990,10 @@
        mel = log_mel_spectrogram(speech).to(self.device)

        if self.asr_model.is_multilingual:
            options = DecodingOptions(fp16=False)
            options = DecodingOptions(fp16=False, language=self.language, task=self.task)
            asr_res = decode(self.asr_model, mel, options)
            text = asr_res.text
            language = asr_res.language
            language = self.language if self.language else asr_res.language
        else:
            asr_res = transcribe(self.asr_model, speech, fp16=False)
            text = asr_res["text"]

 funasr/bin/asr_inference_launch.py

@@ -2056,6 +2056,8 @@

    ncpu = kwargs.get("ncpu", 1)
    torch.set_num_threads(ncpu)
    language = param_dict.get("language", None)
    task = param_dict.get("task", "transcribe")
    if batch_size > 1:
        raise NotImplementedError("batch decoding is not implemented")
    if word_lm_train_config is not None:
@@ -2099,6 +2101,8 @@
        penalty=penalty,
        nbest=nbest,
        streaming=streaming,
        language=language,
        task=task,
    )
    logging.info("speech2text_kwargs: {}".format(speech2text_kwargs))
    speech2text = Speech2TextWhisper(**speech2text_kwargs)