Afrispeech-Whisper-Medium-All
This model builds upon the capabilities of Whisper Medium (a pre-trained model for speech recognition and translation trained on a massive 680k hour dataset). While Whisper demonstrates impressive generalization abilities, this model takes it a step further to be very specific for African accents.
Fine-tuned on the AfriSpeech-200 dataset, specifically designed for African accents, this model offers enhanced performance for speech recognition tasks on African languages.
- Dataset: https://huggingface.co/datasets/intronhealth/afrispeech-200
- Paper: https://arxiv.org/abs/2310.00274
Transcription
In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe).
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("intronhealth/afrispeech-whisper-medium-all")
>>> model = WhisperForConditionalGeneration.from_pretrained("intronhealth/afrispeech-whisper-medium-all")
>>> model.config.forced_decoder_ids = None
>>> # load dummy dataset and read audio files
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>']
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.']
The context tokens can be removed from the start of the transcription by setting skip_special_tokens=True.
Long-Form Transcription
The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking
algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers
pipeline
method. Chunking is enabled by setting chunk_length_s=30 when instantiating the pipeline. With chunking enabled, the pipeline
can be run with batched inference. It can also be extended to predict sequence level timestamps by passing return_timestamps=True:
>>> import torch
>>> from transformers import pipeline
>>> from datasets import load_dataset
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> pipe = pipeline(
>>> "automatic-speech-recognition",
>>> model="intronhealth/afrispeech-whisper-medium-all",
>>> chunk_length_s=30,
>>> device=device,
>>> )
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> prediction = pipe(sample.copy(), batch_size=8)["text"]
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
>>> # we can also return timestamps for the predictions
>>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"]
[{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.',
'timestamp': (0.0, 5.44)}]
Refer to the blog post ASR Chunking for more details on the chunking algorithm.