| from datasets import load_dataset, Audio | |
| English | |
| stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) | |
| stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) | |
| en_sample = next(iter(stream_data))["audio"]["array"] | |
| French | |
| stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "fr", split="test", streaming=True) | |
| stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) | |
| fr_sample = next(iter(stream_data))["audio"]["array"] | |
| Next, we load the model and processor | |
| from transformers import Wav2Vec2ForCTC, AutoProcessor | |
| import torch | |
| model_id = "facebook/mms-1b-all" | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| model = Wav2Vec2ForCTC.from_pretrained(model_id) | |
| Now we process the audio data, pass the processed audio data to the model and transcribe the model output, | |
| just like we usually do for [Wav2Vec2ForCTC]. |