
This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository (`timit-ids.txt`)

The model was finetuned on [Wav2vec 2.0 Base, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec), and the results on the validation set were **PER:** 21\.8%, **FER:** 9\.6%.

{"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"]}
transformers pytorch wav2vec2 automatic-speech-recognition en license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #license-apache-2.0 #endpoints_compatible #region-us 


This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository ('URL')

The model was finetuned on Wav2vec 2.0 Base, No finetuning, and the results on the validation set were PER: 21\.8%, FER: 9\.6%.

TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #license-apache-2.0 #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #license-apache-2.0 #endpoints_compatible #region-us 
