Whisper

Whisper-base for automatic speech recognition (ASR) for the low-resourced Kazakh language. The model was fine-tuned on the Kazakh Speech Corpus 2 with over 1k hours of labelled data. The model achieved 15.36% WER on the test set.

Usage

This checkpoint is a Kazakh-only model, meaning it can be used only for Kazakh speech recognition.

Transcription

>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> import librosa

>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("akuzdeuov/whisper-base.kk")
>>> model = WhisperForConditionalGeneration.from_pretrained("akuzdeuov/whisper-base.kk")

>>> # load your audio
>>> audio, sampling_rate = librosa.load("path_to_audio", sr=16000)
>>> input_features = processor(audio, sampling_rate=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)

>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

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.

>>> import torch
>>> from transformers import pipeline

>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"

>>> pipe = pipeline(
>>>   "automatic-speech-recognition",
>>>   model="akuzdeuov/whisper-base.kk",
>>>   chunk_length_s=30,
>>>   device=device,
>>> )

>>> prediction = pipe("path_to_audio", batch_size=8)["text"]

References

Whisper, OpenAI.

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Transcription

>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> import librosa >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("akuzdeuov/whisper-base.kk") >>> model = WhisperForConditionalGeneration.from_pretrained("akuzdeuov/whisper-base.kk") >>> # load your audio >>> audio, sampling_rate = librosa.load("path_to_audio", sr=16000) >>> input_features = processor(audio, sampling_rate=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) >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

The context tokens can be removed from the start of the transcription by setting skip_special_tokens=True.

Long-Form Transcription

>>> import torch >>> from transformers import pipeline >>> device = "cuda:0" if torch.cuda.is_available() else "cpu" >>> pipe = pipeline( >>> "automatic-speech-recognition", >>> model="akuzdeuov/whisper-base.kk", >>> chunk_length_s=30, >>> device=device, >>> ) >>> prediction = pipe("path_to_audio", batch_size=8)["text"]