Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Instructions to use UrukHan/wav2vec2-russian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UrukHan/wav2vec2-russian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UrukHan/wav2vec2-russian")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("UrukHan/wav2vec2-russian") model = AutoModelForCTC.from_pretrained("UrukHan/wav2vec2-russian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Alikhan Urumov commited on
Commit ·
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Parent(s): 3cb9b83
Update README.md
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README.md
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@@ -38,8 +38,8 @@ should probably proofread and complete it, then remove this comment. -->
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#
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```python
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from transformers import AutoModelForCTC, Wav2Vec2Processor
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model = AutoModelForCTC.from_pretrained("wav2vec2-russian
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processor = Wav2Vec2Processor.from_pretrained("wav2vec2-russian
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def map_to_result(batch):
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with torch.no_grad():
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input_values = torch.tensor(batch["input_values"]).unsqueeze(0) #, device="cuda"
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#
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```python
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from transformers import AutoModelForCTC, Wav2Vec2Processor
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model = AutoModelForCTC.from_pretrained("UrukHan/wav2vec2-russian")
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processor = Wav2Vec2Processor.from_pretrained("UrukHan/wav2vec2-russian")
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def map_to_result(batch):
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with torch.no_grad():
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input_values = torch.tensor(batch["input_values"]).unsqueeze(0) #, device="cuda"
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