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Merge pull request #492 from alibaba-damo-academy/dev_smohan

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add speaker-attributed ASR task for alimeeting
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yhliang 2023-05-11 16:31:40 +08:00 committed by GitHub
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2 changed files with 20 additions and 14 deletions
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18
egs/alimeeting/sa-asr/README.md
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@ -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
@ -65,17 +65,17 @@ The results of the baseline system are as follows. The baseline results include
</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>

16
egs/alimeeting/sa-asr/asr_local.sh
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@ -1226,10 +1226,10 @@ fi
if ${infer_with_pretrained_model}; then
log "Use ${download_sa_asr_model} for decoding and evaluation"
sa_asr_exp="${expdir}/${download_sa_asr_model}"
mkdir -p "${sa_asr_exp}"
python local/download_pretrained_model_from_modelscope.py $download_sa_asr_model ${expdir}
inference_sa_asr_model="model.pb"
inference_config=${sa_asr_exp}/decoding.yaml
@ -1335,8 +1335,11 @@ if ! "${skip_eval}"; then
_data="${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 > ${_dir}/text.cer.txt
@ -1451,8 +1454,11 @@ if ! "${skip_eval}"; then
_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