---
language:
- en
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
widget:
- example_title: Librispeech sample 1
  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: whisper-medium
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Afrispeech-200
      type: intronhealth/afrispeech-200
      config: clean
      split: test
      args:
        language: en
    metrics:
    - type: wer
      value: 0
      name: Test WER
---

# 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).

```python
>>> 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`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) 
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`:

```python
>>> 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](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm.