Instructions to use esc-bench/wav2vec2-ctc-librispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-bench/wav2vec2-ctc-librispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-bench/wav2vec2-ctc-librispeech")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-bench/wav2vec2-ctc-librispeech") model = AutoModelForCTC.from_pretrained("esc-bench/wav2vec2-ctc-librispeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run_librispeech.sh from esc-bench/wav2vec2-ctc-librispeech: direct link, hf CLI and curl.
- Browser
- Download file 945 Bytes
-
https://huggingface.co/esc-bench/wav2vec2-ctc-librispeech/resolve/main/run_librispeech.sh
- Command line
-
hf download hf://esc-bench/wav2vec2-ctc-librispeech/run_librispeech.sh
-
curl -L -o run_librispeech.sh https://huggingface.co/esc-bench/wav2vec2-ctc-librispeech/resolve/main/run_librispeech.sh
945 Bytes
| python run_flax_speech_recognition_ctc.py \ | |
| --model_name_or_path="esb/wav2vec2-ctc-pretrained" \ | |
| --tokenizer_name="wav2vec2-ctc-librispeech-tokenizer" \ | |
| --dataset_name="esb/datasets" \ | |
| --dataset_config_name="librispeech" \ | |
| --output_dir="./" \ | |
| --wandb_project="wav2vec2-ctc" \ | |
| --wandb_name="wav2vec2-ctc-librispeech" \ | |
| --max_steps="50000" \ | |
| --save_steps="10000" \ | |
| --eval_steps="10000" \ | |
| --learning_rate="3e-4" \ | |
| --logging_steps="25" \ | |
| --warmup_steps="5000" \ | |
| --preprocessing_num_workers="1" \ | |
| --hidden_dropout="0.2" \ | |
| --activation_dropout="0.2" \ | |
| --feat_proj_dropout="0.2" \ | |
| --do_train \ | |
| --do_eval \ | |
| --do_predict \ | |
| --overwrite_output_dir \ | |
| --gradient_checkpointing \ | |
| --freeze_feature_encoder \ | |
| --push_to_hub \ | |
| --use_auth_token |