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--- |
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license: cc-by-4.0 |
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datasets: |
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- nwu-ctext/nchlt |
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language: |
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- afr |
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- eng |
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- nbl |
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- nso |
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- sot |
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- ssw |
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- tsn |
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- tso |
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- ven |
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- xho |
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- zul |
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base_model: facebook/mms-1b-all |
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pipeline_tag: automatic-speech-recognition |
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--- |
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Inference Example |
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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 librosa |
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import os |
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model_name = "guymandude/MMS-ASR-ZA-11" |
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def load_audio_file(path): |
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audio_array, sampling_rate = librosa.load(path, sr=None) |
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return {"array": audio_array, "sampling_rate": sampling_rate} |
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model = Wav2Vec2ForCTC.from_pretrained(model_name,ignore_mismatched_sizes=True).to("cuda") |
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processor = Wav2Vec2Processor.from_pretrained(model_name) |
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# change to supported languages [eng, afr, sot, zul, xho, nso, nbl, tso, tsn, ven, ssw] |
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model.load_adapter("tsn") |
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processor.tokenizer.set_target_lang("tsn") |
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audio = load_audio_file("<AUDIO PATH>") |
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input_dict = processor(audio["array"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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logits = model(input_dict.input_values.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1)[0] |
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print(processor.decode(pred_ids)) |
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``` |
