| | |
|---|
| | | rank=$i |
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| | | local_rank=$i |
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| | | gpu_id=$(echo $gpu_devices | cut -d',' -f$[$i+1]) |
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| | | diar_train.py \ |
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| | | python -m funasr.bin.diar_train \ |
|---|
| | | --gpu_id $gpu_id \ |
|---|
| | | --use_preprocessor false \ |
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| | | --token_type char \ |
|---|
| | |
|---|
| | | done |
|---|
| | | fi |
|---|
| | | |
|---|
| | | |
|---|
| | | # You will get a DER like: |
|---|
| | | # iter0: 9.68 10.51 |
|---|
| | | # iter1: |
|---|
| | | # iter2: |
|---|
| | | # iter3: |
|---|
| | | # iter4: |
|---|
| | | # In this stage, we need the raw waveform files of Callhome corpus. |
|---|
| | | # Due to the data license, we can't provide them, please get them additionally. |
|---|
| | | # And convert the sph files to wav files (use scripts/dump_pipe_wav.py). |
|---|
| | | # Then find the wav files to construct wav.scp and put it at data/callhome2/wav.scp. |
|---|
| | | # After iteratively perform SOAP, you will get DER results like: |
|---|
| | | # iters| oracle_vad | system_vad |
|---|
| | | # iter_0: 9.68 | 10.51 |
|---|
| | | # iter_1: 9.26 | 10.14 (reported in the paper) |
|---|
| | | # iter_2: 9.18 | 10.08 |
|---|
| | | # iter_3: 9.24 | 10.15 |
|---|
| | | # iter_4: 9.27 | 10.17 |
|---|
| | | if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then |
|---|
| | | for dset in ${test_sets}; do |
|---|
| | | echo "stage 4: Evaluating finetuned system on ${dset} set with medfilter_size=83 clustering=EEND-OLA" |
|---|
