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--- |
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library_name: transformers |
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tags: |
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- speech-to-txt |
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- uzbek stt |
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- uzbek tts |
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license: apache-2.0 |
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language: |
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- uz |
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pipeline_tag: automatic-speech-recognition |
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--- |
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# Model Card for Model ID |
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This model is a fine-tuned version of oyqiz/uzbek_stt based mainly on laws and military related dataset. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Sara Musaeva |
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- **Funded by:** SSD |
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- **Model type:** Transformers |
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- **Language(s) (NLP):** Uzbek |
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- **Finetuned from model:** Oyqiz/uzbek-stt |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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Intended for Speech-to-text conversion |
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## How to Get Started with the Model |
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```python |
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
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import torch |
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import torchaudio |
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model_name = "sarahai/uzbek-stt-3" |
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model = Wav2Vec2ForCTC.from_pretrained(model_name) |
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processor = Wav2Vec2Processor.from_pretrained(model_name) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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def load_and_preprocess_audio(file_path): |
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speech_array, sampling_rate = torchaudio.load(file_path) |
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if sampling_rate != 16000: |
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resampler = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000) |
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speech_array = resampler(speech_array) |
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return speech_array.squeeze().numpy() |
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def replace_unk(transcription): |
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return transcription.replace("[UNK]", "ʼ") |
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audio_file = "/content/audio_2024-08-13_15-20-53.ogg" |
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speech_array = load_and_preprocess_audio(audio_file) |
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input_values = processor(speech_array, sampling_rate=16000, return_tensors="pt").input_values.to(device) |
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with torch.no_grad(): |
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logits = model(input_values).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(predicted_ids) |
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transcription_text = replace_unk(transcription[0]) |
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print("Transcription:", transcription_text) |
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``` |
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