| | |
| | | # Quick Start |
| | | |
| | | > **Note**: |
| | | > The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take typic model as example to demonstrate the usage. |
| | | |
| | | |
| | | ## Inference with pipeline |
| | | |
| | | ### Speech Recognition |
| | |
| | | |
| | | rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav') |
| | | print(rec_result) |
| | | # {'text': '欢迎大家来体验达摩院推出的语音识别模型'} |
| | | ``` |
| | | |
| | | ### Voice Activity Detection |
| | |
| | | |
| | | segments_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav') |
| | | print(segments_result) |
| | | # {'text': [[70, 2340], [2620, 6200], [6480, 23670], [23950, 26250], [26780, 28990], [29950, 31430], [31750, 37600], [38210, 46900], [47310, 49630], [49910, 56460], [56740, 59540], [59820, 70450]]} |
| | | ``` |
| | | |
| | | ### Punctuation Restoration |
| | |
| | | |
| | | rec_result = inference_pipeline(text_in='我们都是木头人不会讲话不会动') |
| | | print(rec_result) |
| | | # {'text': '我们都是木头人,不会讲话,不会动。'} |
| | | ``` |
| | | |
| | | ### Timestamp Prediction |
| | |
| | | audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_timestamps.wav', |
| | | text_in='一 个 东 太 平 洋 国 家 为 什 么 跑 到 西 太 平 洋 来 了 呢',) |
| | | print(rec_result) |
| | | # {'text': '<sil> 0.000 0.380;一 0.380 0.560;个 0.560 0.800;东 0.800 0.980;太 0.980 1.140;平 1.140 1.260;洋 1.260 1.440;国 1.440 1.680;家 1.680 1.920;<sil> 1.920 2.040;为 2.040 2.200;什 2.200 2.320;么 2.320 2.500;跑 2.500 2.680;到 2.680 2.860;西 2.860 3.040;太 3.040 3.200;平 3.200 3.380;洋 3.380 3.500;来 3.500 3.640;了 3.640 3.800;呢 3.800 4.150;<sil> 4.150 4.440;', 'timestamp': [[380, 560], [560, 800], [800, 980], [980, 1140], [1140, 1260], [1260, 1440], [1440, 1680], [1680, 1920], [2040, 2200], [2200, 2320], [2320, 2500], [2500, 2680], [2680, 2860], [2860, 3040], [3040, 3200], [3200, 3380], [3380, 3500], [3500, 3640], [3640, 3800], [3800, 4150]]} |
| | | ``` |
| | | |
| | | ### Speaker Verification |
| | |
| | | # speaker verification |
| | | rec_result = inference_sv_pipline(audio_in=('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav','https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_same.wav')) |
| | | print(rec_result["scores"][0]) |
| | | # 0.8540499500025098 |
| | | ``` |
| | | |
| | | ### Speaker Diarization |
| | |
| | | text_name="text", |
| | | non_linguistic_symbols=train_args.non_linguistic_symbols, |
| | | ) |
| | | print("start decoding!!!") |
| | | |
| | | @torch.no_grad() |
| | | def __call__(self, text: Union[list, str], split_size=20): |
| | |
| | | result, _ = text2punc(line) |
| | | item = {'key': key, 'value': result} |
| | | results.append(item) |
| | | print(results) |
| | | return results |
| | | |
| | | for inference_text, _, _ in data_path_and_name_and_type: |