| egs/alimeeting/sa-asr/README.md | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs/alimeeting/sa-asr/asr_local.sh | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 |
egs/alimeeting/sa-asr/README.md
@@ -1,7 +1,7 @@ # Get Started Speaker Attributed Automatic Speech Recognition (SA-ASR) is a task proposed to solve "who spoke what". Specifically, the goal of SA-ASR is not only to obtain multi-speaker transcriptions, but also to identify the corresponding speaker for each utterance. The method used in this example is referenced in the paper: [End-to-End Speaker-Attributed ASR with Transformer](https://www.isca-speech.org/archive/pdfs/interspeech_2021/kanda21b_interspeech.pdf). To run this receipe, first you need to install FunASR and ModelScope. ([installation](https://alibaba-damo-academy.github.io/FunASR/en/installation.html)) There are two startup scripts, `run.sh` for training and evaluating on the old eval and test sets, and `run_m2met_2023_infer.sh` for inference on the new test set of the Multi-Channel Multi-Party Meeting Transcription 2.0 ([M2MET2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)) Challenge. There are two startup scripts, `run.sh` for training and evaluating on the old eval and test sets, and `run_m2met_2023_infer.sh` for inference on the new test set of the Multi-Channel Multi-Party Meeting Transcription 2.0 ([M2MeT2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)) Challenge. Before running `run.sh`, you must manually download and unpack the [AliMeeting](http://www.openslr.org/119/) corpus and place it in the `./dataset` directory: ```shell dataset @@ -61,17 +61,17 @@ </tr> <tr> <td>oracle profile</td> <td>31.93</td> <td>32.75</td> <td>48.56</td> <td>53.33</td> <td>32.05</td> <td>32.70</td> <td>47.40</td> <td>52.57</td> </tr> <tr> <td>cluster profile</td> <td>31.94</td> <td>32.77</td> <td>55.49</td> <td>58.17</td> <td>32.05</td> <td>32.70</td> <td>53.76</td> <td>55.95</td> </tr> </table>
@@ -1320,8 +1320,11 @@ _data="${data_feats}/${dset}" _dir="${asr_exp}/${inference_tag}/${dset}" python utils/proce_text.py ${_data}/text ${_data}/text.proc python utils/proce_text.py ${_dir}/text ${_dir}/text.proc sed 's/\$//g' ${_data}/text > ${_data}/text_nosrc sed 's/\$//g' ${_dir}/text > ${_dir}/text_nosrc python utils/proce_text.py ${_data}/text_nosrc ${_data}/text.proc python utils/proce_text.py ${_dir}/text_nosrc ${_dir}/text.proc python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt @@ -1430,8 +1433,11 @@ _data="${data_feats}/${dset}" _dir="${sa_asr_exp}/${sa_asr_inference_tag}.oracle/${dset}" python utils/proce_text.py ${_data}/text ${_data}/text.proc python utils/proce_text.py ${_dir}/text ${_dir}/text.proc sed 's/\$//g' ${_data}/text > ${_data}/text_nosrc sed 's/\$//g' ${_dir}/text > ${_dir}/text_nosrc python utils/proce_text.py ${_data}/text_nosrc ${_data}/text.proc python utils/proce_text.py ${_dir}/text_nosrc ${_dir}/text.proc python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt @@ -1546,8 +1552,11 @@ _data="${data_feats}/${dset}" _dir="${sa_asr_exp}/${sa_asr_inference_tag}.cluster/${dset}" python utils/proce_text.py ${_data}/text ${_data}/text.proc python utils/proce_text.py ${_dir}/text ${_dir}/text.proc sed 's/\$//g' ${_data}/text > ${_data}/text_nosrc sed 's/\$//g' ${_dir}/text > ${_dir}/text_nosrc python utils/proce_text.py ${_data}/text_nosrc ${_data}/text.proc python utils/proce_text.py ${_dir}/text_nosrc ${_dir}/text.proc python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
