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| | | # Get Started |
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| | | 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). |
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| | | To run this receipe, first you need to install FunASR and ModelScope. ([installation](https://alibaba-damo-academy.github.io/FunASR/en/installation.html)) |
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| | | To run this receipe, first you need to install FunASR and ModelScope. ([installation](https://github.com/alibaba-damo-academy/FunASR#installation)) |
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| | | 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. |
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| | | 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: |
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| | | ```shell |
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| | | |—— Train_Ali_far |
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| | | |—— Train_Ali_near |
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| | | ``` |
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| | | There are 18 stages in `run.sh`: |
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| | | There are 16 stages in `run.sh`: |
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| | | ```shell |
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| | | stage 1 - 5: Data preparation and processing. |
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| | | stage 6: Generate speaker profiles (Stage 6 takes a lot of time). |
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