fba5e67d5e
- Introduce unified AMP helpers (create_grad_scaler, torch_autocast) to handle deprecations in PyTorch ≥2.3.0 - Replace direct uses of torch.cuda.amp.GradScaler and torch.cuda.amp.autocast with the new utilities across all training and inference scripts - Update all torch.load calls to include weights_only=False for compatibility with newer PyTorch versions
AMI
This is an ASR recipe for the AMI corpus. AMI provides recordings from the speaker's headset and lapel microphones, and also 2 array microphones containing 8 channels each. We pool data in the following 4 ways and train a single model on the pooled data:
(i) individual headset microphone (IHM) (ii) IHM with simulated reverb (iii) Single distant microphone (SDM) (iv) GSS-enhanced array microphones
Speed perturbation and MUSAN noise augmentation are additionally performed on the pooled data. Here are the statistics of the combined training data:
>>> cuts_train.describe()
Cuts count: 1222053
Total duration (hh:mm:ss): 905:00:28
Speech duration (hh:mm:ss): 905:00:28 (99.9%)
Duration statistics (seconds):
mean 2.7
std 2.8
min 0.0
25% 0.6
50% 1.6
75% 3.8
99% 12.3
99.5% 13.9
99.9% 18.4
max 36.8
Note: This recipe additionally uses GSS for enhancement
of far-field array microphones, but this is optional (see prepare.sh for details).
Performance Record
pruned_transducer_stateless7
The following are decoded using modified_beam_search:
| Evaluation set | dev WER | test WER |
|---|---|---|
| IHM | 18.92 | 17.40 |
| SDM | 31.25 | 32.21 |
| MDM (GSS-enhanced) | 21.67 | 22.43 |
See RESULTS for details.