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db75627e92
* remove unnecessary changes * add AMI prepare scripts * add zipformer scripts for AMI * added logs and pretrained model * minor fix * remove unwanted changes * fix missing link * make suggested changes * update results
49 lines
1.4 KiB
Markdown
49 lines
1.4 KiB
Markdown
# AMI
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This is an ASR recipe for the AMI corpus. AMI provides recordings from the speaker's
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headset and lapel microphones, and also 2 array microphones containing 8 channels each.
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We pool data in the following 4 ways and train a single model on the pooled data:
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(i) individual headset microphone (IHM)
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(ii) IHM with simulated reverb
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(iii) Single distant microphone (SDM)
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(iv) GSS-enhanced array microphones
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Speed perturbation and MUSAN noise augmentation are additionally performed on the pooled
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data. Here are the statistics of the combined training data:
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```python
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>>> cuts_train.describe()
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Cuts count: 1222053
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Total duration (hh:mm:ss): 905:00:28
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Speech duration (hh:mm:ss): 905:00:28 (99.9%)
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Duration statistics (seconds):
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mean 2.7
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std 2.8
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min 0.0
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25% 0.6
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50% 1.6
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75% 3.8
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99% 12.3
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99.5% 13.9
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99.9% 18.4
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max 36.8
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```
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**Note:** This recipe additionally uses [GSS](https://github.com/desh2608/gss) for enhancement
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of far-field array microphones, but this is optional (see `prepare.sh` for details).
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## Performance Record
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### pruned_transducer_stateless7
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The following are decoded using `modified_beam_search`:
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| Evaluation set | dev WER | test WER |
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|--------------------------|------------|---------|
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| IHM | 18.92 | 17.40 |
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| SDM | 31.25 | 32.21 |
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| MDM (GSS-enhanced) | 21.67 | 22.43 |
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See [RESULTS](/egs/ami/ASR/RESULTS.md) for details.
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