From e6d49d086f481465f6e0ce6dc68cc8afbab266e2 Mon Sep 17 00:00:00 2001 From: smohan-speech Date: Thu, 11 May 2023 10:07:32 +0800 Subject: [PATCH] add speaker-attributed ASR task for alimeeting --- egs/alimeeting/sa-asr/README.md | 18 +++++++++--------- egs/alimeeting/sa-asr/asr_local.sh | 21 +++++++++++++++------ 2 files changed, 24 insertions(+), 15 deletions(-) diff --git a/egs/alimeeting/sa-asr/README.md b/egs/alimeeting/sa-asr/README.md index 882345c25..5731c39eb 100644 --- a/egs/alimeeting/sa-asr/README.md +++ b/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 @@ The results of the baseline system are as follows. The baseline results include oracle profile - 31.93 - 32.75 - 48.56 - 53.33 + 32.05 + 32.70 + 47.40 + 52.57 cluster profile - 31.94 - 32.77 - 55.49 - 58.17 + 32.05 + 32.70 + 53.76 + 55.95 diff --git a/egs/alimeeting/sa-asr/asr_local.sh b/egs/alimeeting/sa-asr/asr_local.sh index f8cdcd3b6..389bb0158 100755 --- a/egs/alimeeting/sa-asr/asr_local.sh +++ b/egs/alimeeting/sa-asr/asr_local.sh @@ -1320,8 +1320,11 @@ if ! "${skip_eval}"; then _data="${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 > ${_dir}/text.cer.txt @@ -1430,8 +1433,11 @@ if ! "${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 > ${_dir}/text.cer.txt @@ -1546,8 +1552,11 @@ if ! "${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