whisper-tiny-cantonese

<p align="left"> 🤗 <a href="https://huggingface.co/Oblivion208" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/fengredrum/finetune-whisper-lora" target="_blank">Github Repo</a> </p>

Usage

import torch
import librosa
from transformers import WhisperProcessor, WhisperTokenizer, WhisperForConditionalGeneration

# Setups
model_name_or_path = "Oblivion208/whisper-tiny-cantonese"
task = "transcribe"
device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path).to(device)
tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path, task=task)
processor = WhisperProcessor.from_pretrained(model_name_or_path, task=task)
feature_extractor = processor.feature_extractor
model.config.forced_decoder_ids = None
model.config.suppress_tokens = []

filepath = 'test.wav'
audio, sr = librosa.load(filepath, sr=16000, mono=True)
inputs = processor(audio, sample_rate=sr, return_tensors="pt").to(device)

with torch.inference_mode():
    generated_tokens = model.generate(
        input_features=inputs.input_features,
        return_dict_in_generate=True,
        max_new_tokens=255,
    )
    transcription = tokenizer.batch_decode(
        generated_tokens.sequences, skip_special_tokens=True)
    print(transcription)

Approximate Performance Evaluation

The following models are all trained and evaluated on a single RTX 3090 GPU.

Cantonese Test Results Comparison

MDCC

Common Voice Corpus 11.0