Metadata-Version: 2.1
Name: funasr_torch
Version: 0.0.3
Summary: FunASR: A Fundamental End-to-End Speech Recognition Toolkit
Home-page: https://github.com/alibaba-damo-academy/FunASR.git
Author: Speech Lab, Alibaba Group, China
Author-email: funasr@list.alibaba-inc.com
License: The MIT License
Keywords: funasr,paraformer, funasr_torch
Platform: Any
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown

## Using funasr with libtorch

[FunASR](https://github.com/alibaba-damo-academy/FunASR) hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model released on ModelScope, researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun！

### Introduction
- Model comes from [speech_paraformer](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary).

### Steps:
1. Export the model.
   - Command: (`Tips`: torch >= 1.11.0 is required.)

       More details ref to ([export docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export))

       - `e.g.`, Export model from modelscope
         ```shell
         python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch --quantize False
         ```
       - `e.g.`, Export model from local path, the model'name must be `model.pb`.
         ```shell
         python -m funasr.export.export_model --model-name ./damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch --quantize False
         ```


2. Install the `funasr_torch`.
    ```shell
    pip install funasr_torch -i https://pypi.Python.org/simple
    ```


3. Run the demo.
   - Model_dir: the model path, which contains `model.torchscripts`, `config.yaml`, `am.mvn`.
   - Input: wav formt file, support formats: `str, np.ndarray, List[str]`
   - Output: `List[str]`: recognition result.
   - Example:
        ```python
        from funasr_torch import Paraformer

        model_dir = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
        model = Paraformer(model_dir, batch_size=1)

        wav_path = ['/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']

        result = model(wav_path)
        print(result)
        ```

## Speed

Environment：Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz

Test [wav, 5.53s, 100 times avg.](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav)

| Backend  | RTF (FP32) |
|:--------:|:----------:|
| Pytorch  |   0.110    |
| Libtorch |   0.048    |
|   Onnx   |   0.038    |

## Acknowledge
This project is maintained by [FunASR community](https://github.com/alibaba-damo-academy/FunASR).
