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 @@ def modelscope_infer(args):
 
 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 @@ set -o pipefail
 
 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 @@ class Speech2TextWhisper:
             nbest: int = 1,
             streaming: bool = False,
             frontend_conf: dict = None,
+            language: str = None,
+            task: str = "transcribe",
             **kwargs,
     ):
 
@@ -1960,6 +1962,8 @@ class Speech2TextWhisper:
         self.device = device
         self.dtype = dtype
         self.frontend = frontend
+        self.language = language
+        self.task = task
 
     @torch.no_grad()
     def __call__(
@@ -1986,10 +1990,10 @@ class Speech2TextWhisper:
         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 @@ def inference_whisper(
 
     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 @@ def inference_whisper(
         penalty=penalty,
         nbest=nbest,
         streaming=streaming,
+        language=language,
+        task=task,
     )
     logging.info("speech2text_kwargs: {}".format(speech2text_kwargs))
     speech2text = Speech2TextWhisper(**speech2text_kwargs)