Instructions to use marinone94/whisper-tiny-sv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marinone94/whisper-tiny-sv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="marinone94/whisper-tiny-sv")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("marinone94/whisper-tiny-sv") model = AutoModelForMultimodalLM.from_pretrained("marinone94/whisper-tiny-sv") - Notebooks
- Google Colab
- Kaggle
| python $1run_speech_recognition_seq2seq_streaming.py \ | |
| --model_name_or_path="openai/whisper-tiny" \ | |
| --dataset_train_name="mozilla-foundation/common_voice_11_0,mozilla-foundation/common_voice_11_0,mozilla-foundation/common_voice_11_0" \ | |
| --dataset_train_config_name="sv-SE,da,nn-NO" \ | |
| --language_train="swedish,danish,norwegian" \ | |
| --train_split_name="train+validation,train+validation,train+validation" \ | |
| --dataset_eval_name="mozilla-foundation/common_voice_11_0,mozilla-foundation/common_voice_11_0,mozilla-foundation/common_voice_11_0" \ | |
| --dataset_eval_config_name="sv-SE,da,nn-NO" \ | |
| --language_eval="swedish,danish,norwegian" \ | |
| --eval_split_name="test" \ | |
| --model_index_name="Whisper Tiny Swedish" \ | |
| --max_train_samples="64" \ | |
| --max_eval_samples="32" \ | |
| --max_steps="500" \ | |
| --output_dir="./" \ | |
| --per_device_train_batch_size="8" \ | |
| --per_device_eval_batch_size="4" \ | |
| --logging_steps="25" \ | |
| --learning_rate="1e-5" \ | |
| --warmup_steps="500" \ | |
| --evaluation_strategy="steps" \ | |
| --eval_steps="1000" \ | |
| --save_strategy="steps" \ | |
| --save_steps="1000" \ | |
| --generation_max_length="225" \ | |
| --length_column_name="input_length" \ | |
| --max_duration_in_seconds="30" \ | |
| --text_column_name="sentence,text" \ | |
| --freeze_feature_encoder="False" \ | |
| --metric_for_best_model="wer" \ | |
| --greater_is_better="False" \ | |
| --load_best_model_at_end \ | |
| --gradient_checkpointing \ | |
| --overwrite_output_dir \ | |
| --do_train \ | |
| --do_eval \ | |
| --predict_with_generate \ | |
| --do_normalize_eval \ | |
| --streaming \ | |
| --use_auth_token \ | |
| --push_to_hub | |