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| | | stage 7 - 9: Language model training (Optional). |
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| | | stage 10 - 11: ASR training (SA-ASR requires loading the pre-trained ASR model). |
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| | | stage 12: SA-ASR training. |
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| | | stage 13 - 18: Inference and evaluation. |
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| | | stage 13 - 16: Inference and evaluation. |
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| | | ``` |
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| | | 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. |
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| | | ```shell |
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| | |
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| | | stage 3: Inference. |
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| | | stage 4: Generation of SA-ASR results required for final submission. |
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| | | ``` |
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| | | |
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| | | The baseline model is available on [ModelScope](https://www.modelscope.cn/models/damo/speech_saasr_asr-zh-cn-16k-alimeeting/summary). |
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| | | 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. |
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| | | |
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| | | # Format of Final Submission |
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| | | Finally, you need to submit a file called `text_spk_merge` with the following format: |
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| | | ```shell |
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