06f1fd69a6
4.4 KiB
This model was published in HF papers on 2022-12-06 and contributed to Hugging Face Transformers on 2022-10-05.
Whisper
Whisper is an encoder-decoder (sequence-to-sequence) transformer pretrained on 680,000 hours of labeled audio data. This amount of pretraining data enables zero-shot performance on audio tasks in English and many other languages. The decoder allows Whisper to map the encoders learned speech representations to useful outputs, such as text, without additional fine-tuning. Whisper just works out of the box.
You can find all the original Whisper checkpoints under the Whisper collection.
Tip
Click on the Whisper models in the right sidebar for more examples of how to apply Whisper to different audio tasks.
The example below demonstrates how to automatically transcribe speech into text with [Pipeline] or the [AutoModel] class.
from transformers import pipeline
pipeline = pipeline(
task="automatic-speech-recognition",
model="openai/whisper-large-v3-turbo",
device=0
)
pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
# pip install datasets
from datasets import load_dataset
from transformers import AutoProcessor, WhisperForConditionalGeneration
processor = AutoProcessor.from_pretrained(
"openai/whisper-large-v3-turbo",
)
model = WhisperForConditionalGeneration.from_pretrained(
"openai/whisper-large-v3-turbo",
device_map="auto",
attn_implementation="sdpa"
)
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
audio_sample = ds[0]["audio"]
input_features = processor(
audio_sample["array"],
sampling_rate=audio_sample["sampling_rate"],
return_tensors="pt"
).input_features
input_features = input_features.to(model.device)
predicted_ids = model.generate(input_features, cache_implementation="static")
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
transcription[0]
Notes
- Whisper relies on a custom [
generate] for inference, make sure to check the docs below. - The [
WhisperProcessor] can be used for preparing audio and decoding predicted ids back into text.
WhisperConfig
autodoc WhisperConfig
WhisperTokenizer
autodoc WhisperTokenizer - set_prefix_tokens - get_special_tokens_mask - save_vocabulary - batch_decode - decode - basic_normalize - normalize
WhisperTokenizerFast
autodoc WhisperTokenizerFast - set_prefix_tokens - get_special_tokens_mask - save_vocabulary - batch_decode - decode - basic_normalize - normalize
WhisperFeatureExtractor
autodoc WhisperFeatureExtractor - call
WhisperProcessor
autodoc WhisperProcessor - call - from_pretrained - save_pretrained - batch_decode - decode
WhisperModel
autodoc WhisperModel - forward - _mask_input_features
WhisperForConditionalGeneration
autodoc WhisperForConditionalGeneration - forward - generate
WhisperForCausalLM
autodoc WhisperForCausalLM - forward
WhisperForAudioClassification
autodoc WhisperForAudioClassification - forward