Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

游雁

2023-02-13 de264be0936d14980397641e86520ca6526a891a

Merge branch 'main' of github.com:alibaba-damo-academy/FunASR
add

 egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/README.md

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@@ -0,0 +1,19 @@
# ModelScope Model

## How to infer using a pretrained Paraformer-large Model

### Inference

You can use the pretrain model for inference directly.

- Setting parameters in `infer.py`
    - <strong>audio_in:</strong> # Support wav, url, bytes, and parsed audio format.
    - <strong>output_dir:</strong> # If the input format is wav.scp, it needs to be set.
    - <strong>batch_size:</strong> # Set batch size in inference.
    - <strong>param_dict:</strong> # Set the hotword list in inference.

- Then you can run the pipeline to infer with:
```python
    python infer.py
```


 egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer.py

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@@ -0,0 +1,21 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks


if __name__ == '__main__':
    param_dict = dict()
    param_dict['hotword'] = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/hotword.txt"

    audio_in = "//isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_hotword.wav"
    output_dir = None
    batch_size = 1

    inference_pipeline = pipeline(
        task=Tasks.auto_speech_recognition,
        model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404",
        output_dir=output_dir,
        batch_size=batch_size,
        param_dict=param_dict)

    rec_result = inference_pipeline(audio_in=audio_in)
    print(rec_result)

 funasr/bin/asr_inference_paraformer.py

@@ -6,6 +6,8 @@
import copy
import os
import codecs
import tempfile
import requests
from pathlib import Path
from typing import Optional
from typing import Sequence
@@ -175,10 +177,24 @@
        self.converter = converter
        self.tokenizer = tokenizer

        # 6. [Optional] Build hotword list from file or str
        # 6. [Optional] Build hotword list from str, local file or url
        # for None 
        if hotword_list_or_file is None:
            self.hotword_list = None
        # for text str input
        elif not os.path.exists(hotword_list_or_file) and not hotword_list_or_file.startswith('http'):
            logging.info("Attempting to parse hotwords as str...")
            self.hotword_list = []
            hotword_str_list = []
            for hw in hotword_list_or_file.strip().split():
                hotword_str_list.append(hw)
                self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
            self.hotword_list.append([self.asr_model.sos])
            hotword_str_list.append('<s>')
            logging.info("Hotword list: {}.".format(hotword_str_list))
        # for local txt inputs
        elif os.path.exists(hotword_list_or_file):
            logging.info("Attempting to parse hotwords from local txt...")
            self.hotword_list = []
            hotword_str_list = []
            with codecs.open(hotword_list_or_file, 'r') as fin:
@@ -186,20 +202,31 @@
                    hw = line.strip()
                    hotword_str_list.append(hw)
                    self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
                self.hotword_list.append([1])
                self.hotword_list.append([self.asr_model.sos])
                hotword_str_list.append('<s>')
            logging.info("Initialized hotword list from file: {}, hotword list: {}."
                .format(hotword_list_or_file, hotword_str_list))
        # for url, download and generate txt
        else:
            logging.info("Attempting to parse hotwords as str...")
            logging.info("Attempting to parse hotwords from url...")
            work_dir = tempfile.TemporaryDirectory().name
            if not os.path.exists(work_dir):
                os.makedirs(work_dir)
            text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
            local_file = requests.get(hotword_list_or_file)
            open(text_file_path, "wb").write(local_file.content)
            hotword_list_or_file = text_file_path
            self.hotword_list = []
            hotword_str_list = []
            for hw in hotword_list_or_file.strip().split():
                hotword_str_list.append(hw)
                self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
            self.hotword_list.append([1])
            hotword_str_list.append('<s>')
            logging.info("Hotword list: {}.".format(hotword_str_list))
            with codecs.open(hotword_list_or_file, 'r') as fin:
                for line in fin.readlines():
                    hw = line.strip()
                    hotword_str_list.append(hw)
                    self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
                self.hotword_list.append([self.asr_model.sos])
                hotword_str_list.append('<s>')
            logging.info("Initialized hotword list from file: {}, hotword list: {}."
                .format(hotword_list_or_file, hotword_str_list))


        is_use_lm = lm_weight != 0.0 and lm_file is not None