Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
wav2vec2
google/fleurs
Generated from Trainer
pashto
ps
Eval Results (legacy)
Instructions to use ihanif/wav2vec2-xls-r-300m-pashto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ihanif/wav2vec2-xls-r-300m-pashto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ihanif/wav2vec2-xls-r-300m-pashto")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ihanif/wav2vec2-xls-r-300m-pashto") model = AutoModelForCTC.from_pretrained("ihanif/wav2vec2-xls-r-300m-pashto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| python run_speech_recognition_ctc.py \ | |
| --dataset_name="google/fleurs" \ | |
| --dataset_config_name="ps_af" \ | |
| --model_name_or_path="facebook/wav2vec2-xls-r-300m" \ | |
| --output_dir="./" \ | |
| --overwrite_output_dir="False" \ | |
| --max_steps="4500" \ | |
| --per_device_train_batch_size="8" \ | |
| --per_device_eval_batch_size="8" \ | |
| --gradient_accumulation_steps="4" \ | |
| --learning_rate="7.5e-5" \ | |
| --warmup_steps="2000" \ | |
| --evaluation_strategy="steps" \ | |
| --text_column_name="transcription" \ | |
| --save_steps="500" \ | |
| --eval_steps="500" \ | |
| --logging_steps="10" \ | |
| --layerdrop="0.0" \ | |
| --activation_dropout="0.1" \ | |
| --eval_metrics wer cer \ | |
| --greater_is_better="False" \ | |
| --load_best_model_at_end \ | |
| --save_total_limit="3" \ | |
| --mask_time_prob="0.3" \ | |
| --mask_time_length="10" \ | |
| --mask_feature_prob="0.1" \ | |
| --fp16 \ | |
| --mask_feature_length="64" \ | |
| --chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \ | |
| --group_by_length \ | |
| --push_to_hub \ | |
| --do_train --do_eval \ | |
| --gradient_checkpointing \ | |
| --use_auth_token | |
| --freeze_feature_extractor="True" | |