
Second fine-tuning try of `wav2vec2-base`. Results are similar to the ones reported in https://huggingface.co/facebook/wav2vec2-base-100h.

Model was trained on *librispeech-clean-train.100* with following hyper-parameters:

- 2 GPUs Titan RTX
- Total update steps 11000
- Batch size per GPU: 32 corresponding to a *total batch size* of ca. ~750 seconds
- Adam with linear decaying learning rate with 3000 warmup steps
- dynamic padding for batch
- fp16
- attention_mask was **not** used during training

Check: https://wandb.ai/patrickvonplaten/huggingface/runs/1yrpescx?workspace=user-patrickvonplaten

*Result (WER)* on Librispeech:

| "clean" (% rel difference to results in paper) | "other" (% rel difference to results in paper) |
|---|---|
| 6.2 (-1.6%) | 15.2 (-11.2%)|
{"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "IEMOCAP sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav"}, {"example_title": "IEMOCAP sample 2", "src": "https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro04_F000.wav"}, {"example_title": "LibriSpeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac"}, {"example_title": "LibriSpeech sample 2", "src": "https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0001.flac"}, {"example_title": "VoxCeleb sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav"}, {"example_title": "VoxCeleb sample 2", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav"}]}
transformers pytorch wav2vec2 automatic-speech-recognition audio en dataset:librispeech_asr license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us 

Second fine-tuning try of 'wav2vec2-base'. Results are similar to the ones reported in URL


Model was trained on *librispeech-clean-train.100* with following hyper-parameters:


* 2 GPUs Titan RTX
* Total update steps 11000
* Batch size per GPU: 32 corresponding to a *total batch size* of ca. ~750 seconds
* Adam with linear decaying learning rate with 3000 warmup steps
* dynamic padding for batch
* fp16
* attention\_mask was not used during training


Check: URL


*Result (WER)* on Librispeech:




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

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