| | |
|---|
| | |  |
|---|
| | | |
|---|
| | | ## Quick start |
|---|
| | | To run the baseline, first you need to install FunASR and ModelScope. ([installation](https://alibaba-damo-academy.github.io/FunASR/en/installation.html)) |
|---|
| | | To run the baseline, 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 |
|---|
| | |
|---|
| | | ## Baseline results |
|---|
| | | The results of the baseline system are shown in Table 3. The speaker profile adopts the oracle speaker embedding during training. However, due to the lack of oracle speaker label during evaluation, the speaker profile provided by an additional spectral clustering is used. Meanwhile, the results of using the oracle speaker profile on Eval and Test Set are also provided to show the impact of speaker profile accuracy. |
|---|
| | | |
|---|
| | |  |
|---|
| | | |
|---|
| | | | |SI-CER(%) |cpCER(%) | |
|---|
| | | |:---------------|:------------:|----------:| |
|---|
| | | |oracle profile |32.72 |42.92 | |
|---|
| | | |cluster profile|32.73 |49.37 | |
|---|
