From e1ba6bc138b4e73875c64f35f98f3b15a0560e92 Mon Sep 17 00:00:00 2001 From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com> Date: 星期三, 17 五月 2023 15:16:06 +0800 Subject: [PATCH] Merge branch 'dev_infer' of https://github.com/alibaba/FunASR into dev_infer --- egs/alimeeting/sa-asr/README.md | 10 +++++++--- 1 files changed, 7 insertions(+), 3 deletions(-) diff --git a/egs/alimeeting/sa-asr/README.md b/egs/alimeeting/sa-asr/README.md index 5731c39..2ef6bbe 100644 --- a/egs/alimeeting/sa-asr/README.md +++ b/egs/alimeeting/sa-asr/README.md @@ -1,6 +1,6 @@ # 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)) +To run this receipe, first you need to install FunASR and ModelScope. ([installation](https://github.com/alibaba-damo-academy/FunASR#installation)) 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 @@ -12,14 +12,14 @@ |鈥斺�� Train_Ali_far |鈥斺�� Train_Ali_near ``` -There are 18 stages in `run.sh`: +There are 16 stages in `run.sh`: ```shell stage 1 - 5: Data preparation and processing. stage 6: Generate speaker profiles (Stage 6 takes a lot of time). stage 7 - 9: Language model training (Optional). stage 10 - 11: ASR training (SA-ASR requires loading the pre-trained ASR model). stage 12: SA-ASR training. -stage 13 - 18: Inference and evaluation. +stage 13 - 16: Inference and evaluation. ``` Before running `run_m2met_2023_infer.sh`, you need to place the new test set `Test_2023_Ali_far` (to be released after the challenge starts) in the `./dataset` directory, which contains only raw audios. Then put the given `wav.scp`, `wav_raw.scp`, `segments`, `utt2spk` and `spk2utt` in the `./data/Test_2023_Ali_far` directory. ```shell @@ -37,6 +37,10 @@ stage 3: Inference. stage 4: Generation of SA-ASR results required for final submission. ``` + +The baseline model is available on [ModelScope](https://www.modelscope.cn/models/damo/speech_saasr_asr-zh-cn-16k-alimeeting/summary). +After generate stats of AliMeeting corpus(stage 10 in `run.sh`), you can set the `infer_with_pretrained_model=true` in `run.sh` to infer with our official baseline model released on ModelScope without training. + # Format of Final Submission Finally, you need to submit a file called `text_spk_merge` with the following format: ```shell -- Gitblit v1.9.1