eddiegulay_wav2vec2-large-xlsr-mvc-swahili

---

license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags:

• generated_from_trainer datasets:
• common_voice_13_0 metrics:
• wer model-index:
• name: wav2vec2-large-xlsr-mvc-swahili results:
  • task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_13_0 type: common_voice_13_0 config: sw split: test args: sw metrics:
    • name: Wer type: wer value: 0.2

language:

• sw

---

wav2vec2-large-xlsr-mvc-swahili

This model is a finetuned version of facebook/wav2vec2-large-xlsr-53.

How to use the model

There was an issue with vocab, seems like there are special characters included and they were not considered during training
You could try

from transformers import AutoProcessor, AutoModelForCTC

repo_name = "eddiegulay/wav2vec2-large-xlsr-mvc-swahili"
processor = AutoProcessor.from_pretrained(repo_name)
model = AutoModelForCTC.from_pretrained(repo_name)

# if you have GPU
# move model to CUDA
model = model.to("cuda")


def transcribe(audio_path):
  # Load the audio file
  audio_input, sample_rate = torchaudio.load(audio_path)
  target_sample_rate = 16000
  audio_input = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)(audio_input)

  # Preprocess the audio data
  input_dict = processor(audio_input[0], return_tensors="pt", padding=True, sampling_rate=16000)

  # Perform inference and transcribe
  logits = model(input_dict.input_values.to("cuda")).logits
  pred_ids = torch.argmax(logits, dim=-1)[0]
  transcription = processor.decode(pred_ids)

  return transcription

transcript = transcribe('your_audio.mp3')