---
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_13_0
language:
- sw
library_name: transformers
tags:
- language
- swahili
- asr
- mozilla
---

## How To Use

Here is a plug and play inference code
```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration

processor = WhisperProcessor.from_pretrained("eddiegulay/Whisperer_Mozilla_Sw_2000")
model = WhisperForConditionalGeneration.from_pretrained("eddiegulay/Whisperer_Mozilla_Sw_2000")
forced_decoder_ids = processor.get_decoder_prompt_ids(language="swahili", task="transcribe")

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_features = processor(audio_input[0], sampling_rate=target_sample_rate, return_tensors="pt").input_features

  # generate token ids
  predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)

  # Perform inference and transcribe
  transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

  return transcription



transcribe('your_audio_file.mp3')
```