eddiegulay/Whisperer_Mozilla_Sw_2000

🤗 Hugging Face sourceautomatic-speech-recognitionapache-2.0764M params3.1 GBsafetensors✓ 2 checksumsupdated today

Submit in one command

Run it next to your model folder. It makes the torrent, checks your files against Hugging Face, and submits it. You just start seeding and paste your key from your account. It only reads your files and never changes them. Read the script first if you like.

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo eddiegulay/Whisperer_Mozilla_Sw_2000 ./model-folder

Needs a seeder →

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')