How To Use
Here is a plug and play inference code
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')