Instructions to use esb/wav2vec2-aed-chime4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esb/wav2vec2-aed-chime4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esb/wav2vec2-aed-chime4")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esb/wav2vec2-aed-chime4") model = AutoModelForSpeechSeq2Seq.from_pretrained("esb/wav2vec2-aed-chime4") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
tags:
- esb
datasets:
- esb/datasets
- ldc/chime-4
To reproduce this run, execute:
#!/usr/bin/env bash
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