32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
from transformers import AutoProcessor, CohereAsrForConditionalGeneration
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from transformers.audio_utils import load_audio
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from huggingface_hub import hf_hub_download
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# Load model
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print("Loading model...")
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processor = AutoProcessor.from_pretrained("CohereLabs/cohere-transcribe-03-2026")
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model = CohereAsrForConditionalGeneration.from_pretrained(
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"CohereLabs/cohere-transcribe-03-2026",
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device_map="auto"
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)
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def transcribe_audio(audio, language="en"):
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inputs = processor(audio, sampling_rate=16000, return_tensors="pt", language=language)
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inputs.to(model.device, dtype=model.dtype)
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outputs = model.generate(**inputs, max_new_tokens=256)
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text = processor.decode(outputs, skip_special_tokens=True)
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return text
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# Use demo audio file from Hugging Face
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print("Loading demo audio...")
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audio_file = hf_hub_download(
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repo_id="CohereLabs/cohere-transcribe-03-2026",
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filename="demo/voxpopuli_test_en_demo.wav",
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)
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audio = load_audio(audio_file, sampling_rate=16000)
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print("Transcribing...")
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text = transcribe_audio(audio)
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print(f"\nTranscription:\n{text}\n")
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