Instructions to use lsnoo/russian_fairseq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lsnoo/russian_fairseq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lsnoo/russian_fairseq")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lsnoo/russian_fairseq") model = AutoModelForCTC.from_pretrained("lsnoo/russian_fairseq") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Wav2vec2.0-xlsr-53 model is fine-tuned on commonvoice russian dataset
Configs (yaml)
checkpoint: save_interval: 1000 save_interval_updates: 1000 keep_interval_updates: 1 no_epoch_ckechpoints: true best_checkpoint_metric: wer
task: _name: audio_finetuning normalize: true labels: phn
dataset: num_workers: 6 max_tokens: 800000 skip_invalid_size_inputs_valid_test: true valid_subset: valie
distributed_training: ddp_backend: legacy_ddp distributed_world_size: 4
criterion: _name: ctc zero_infinity: true
optimization: max_update: 25000 lr: [0.00001] sentence_avg: true update_freq: [4]
optimizer: _name: adam adam_betas: (0.9, 0.98) adam_eps: 1e-8
lr_scheduler: _name: tri_stage phase_ratio: [0.1, 0.4, 0.5] final_lr_scale: 0.05
model: _name: wav2vec_ctc apply_mask: true mask_prob: 0.5 mask_channel_prob: 0.1 mask_channel_length: 64 layerdrop: 0.1 activation_dropout: 0.1 feature_grad_mult: 0.0 freeze_finetune_updates: 0
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