Instructions to use esc-bench/wav2vec2-aed-chime4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-bench/wav2vec2-aed-chime4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-bench/wav2vec2-aed-chime4")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-bench/wav2vec2-aed-chime4") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-bench/wav2vec2-aed-chime4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| python run_flax_speech_recognition_seq2seq.py \ | |
| --dataset_name="esb/datasets" \ | |
| --model_name_or_path="esb/wav2vec2-aed-pretrained" \ | |
| --dataset_config_name="chime4" \ | |
| --output_dir="./" \ | |
| --wandb_name="wav2vec2-aed-chime4" \ | |
| --wandb_project="wav2vec2-aed" \ | |
| --per_device_train_batch_size="8" \ | |
| --per_device_eval_batch_size="4" \ | |
| --logging_steps="25" \ | |
| --max_steps="50001" \ | |
| --eval_steps="10000" \ | |
| --save_steps="10000" \ | |
| --generation_max_length="40" \ | |
| --generation_num_beams="1" \ | |
| --final_generation_max_length="250" \ | |
| --final_generation_num_beams="5" \ | |
| --generation_length_penalty="0.6" \ | |
| --learning_rate="1e-4" \ | |
| --warmup_steps="500" \ | |
| --hidden_dropout="0.2" \ | |
| --activation_dropout="0.2" \ | |
| --feat_proj_dropout="0.2" \ | |
| --overwrite_output_dir \ | |
| --gradient_checkpointing \ | |
| --freeze_feature_encoder \ | |
| --predict_with_generate \ | |
| --do_eval \ | |
| --do_train \ | |
| --do_predict \ | |
| --push_to_hub \ | |
| --use_auth_token |