Instructions to use sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation") model = AutoModelForSpeechSeq2Seq.from_pretrained("sanchit-gandhi/flax-wav2vec2-2-bart-large-cnn-gradient-accumulation") - Notebooks
- Google Colab
- Kaggle
| python run_flax_speech_recognition_seq2seq.py \ | |
| --dataset_name="librispeech_asr" \ | |
| --model_name_or_path="./" \ | |
| --dataset_config_name="clean" \ | |
| --train_split_name="train.100" \ | |
| --eval_split_name="validation" \ | |
| --output_dir="./" \ | |
| --dataset_cache_dir="/home/sanchit_huggingface_co/cache/huggingface/datasets" \ | |
| --cache_dir="/home/sanchit_huggingface_co/cache/huggingface/transformers" \ | |
| --preprocessing_num_workers="16" \ | |
| --length_column_name="input_length" \ | |
| --overwrite_output_dir \ | |
| --num_train_epochs="5" \ | |
| --per_device_train_batch_size="4" \ | |
| --per_device_eval_batch_size="4" \ | |
| --gradient_accumulation_steps="2" \ | |
| --logging_steps="25" \ | |
| --max_duration_in_seconds="15" \ | |
| --max_target_length="64" \ | |
| --generation_max_length="40" \ | |
| --generation_num_beams="1" \ | |
| --learning_rate="3e-4" \ | |
| --warmup_steps="500" \ | |
| --text_column_name="text" \ | |
| --save_total_limit="1" \ | |
| --freeze_feature_encoder \ | |
| --predict_with_generate \ | |
| --do_lower_case \ | |
| --do_eval \ | |
| --do_train \ | |
| --push_to_hub \ | |
| --use_auth_token | |