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.