Whisper Large V3

---
language:

• en

• zh

• de

• es

• ru

• ko

• fr

• ja

• pt

• tr

• pl

• ca

• nl

• ar

• sv

• it

• id

• hi

• fi

• vi

• he

• uk

• el

• ms

• cs

• ro

• da

• hu

• ta

• no

• th

• ur

• hr

• bg

• lt

• la

• mi

• ml

• cy

• sk

• te

• fa

• lv

• bn

• sr

• az

• sl

• kn

• et

• mk

• br

• eu

• is

• hy

• ne

• mn

• bs

• kk

• sq

• sw

• gl

• mr

• pa

• si

• km

• sn

• yo

• so

• af

• oc

• ka

• be

• tg

• sd

• gu

• am

• yi

• lo

• uz

• fo

• ht

• ps

• tk

• nn

• mt

• sa

• lb

• my

• bo

• tl

• mg

• as

• tt

• haw

• ln

• ha

• ba

• jw

• su

• audio

• automatic-speech-recognition

• hf-asr-leaderboard

• example_title: Librispeech sample 1

• example_title: Librispeech sample 2

Whisper

Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper
Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford
et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many
datasets and domains in a zero-shot setting.

Whisper large-v3 has the same architecture as the previous large and large-v2
models, except for the following minor differences:

1. The spectrogram input uses 128 Mel frequency bins instead of 80
2. A new language token for Cantonese

The Whisper large-v3 model was trained on 1 million hours of weakly labeled audio and 4 million hours of pseudo-labeled
audio collected using Whisper large-v2 . The model was trained for 2.0 epochs over this mixture dataset.

The large-v3 model shows improved performance over a wide variety of languages, showing 10% to 20% reduction of errors
compared to Whisper large-v2 . For more details on the different checkpoints available, refer to the section Model details.

Disclaimer: Content for this model card has partly been written by the 🤗 Hugging Face team, and partly copied and
pasted from the original model card.

Usage

Whisper large-v3 is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers
library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub, and
🤗 Accelerate to reduce the model loading time:

pip install --upgrade pip
pip install --upgrade transformers datasets[audio] accelerate

The model can be used with the pipeline
class to transcribe audios of arbitrary length:

import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "openai/whisper-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    torch_dtype=torch_dtype,
    device=device,
)
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])

To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline:

result = pipe("audio.mp3")

Multiple audio files can be transcribed in parallel by specifying them as a list and setting the batch_size parameter:

result = pipe(["audio_1.mp3", "audio_2.mp3"], batch_size=2)

Transformers is compatible with all Whisper decoding strategies, such as temperature fallback and condition on previous
tokens. The following example demonstrates how to enable these heuristics:

generate_kwargs = {
    "max_new_tokens": 448,
    "num_beams": 1,
    "condition_on_prev_tokens": False,
    "compression_ratio_threshold": 1.35,  # zlib compression ratio threshold (in token space)
    "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
    "logprob_threshold": -1.0,
    "no_speech_threshold": 0.6,
    "return_timestamps": True,
}
result = pipe(sample, generate_kwargs=generate_kwargs)

Whisper predicts the language of the source audio automatically. If the source audio language is known *a-priori*, it
can be passed as an argument to the pipeline:

result = pipe(sample, generate_kwargs={"language": "english"})

By default, Whisper performs the task of *speech transcription*, where the source audio language is the same as the target
text language. To perform *speech translation*, where the target text is in English, set the task to "translate":

result = pipe(sample, generate_kwargs={"task": "translate"})

Finally, the model can be made to predict timestamps. For sentence-level timestamps, pass the return_timestamps argument:

result = pipe(sample, return_timestamps=True)
print(result["chunks"])

And for word-level timestamps:

result = pipe(sample, return_timestamps="word")
print(result["chunks"])

The above arguments can be used in isolation or in combination. For example, to perform the task of speech transcription
where the source audio is in French, and we want to return sentence-level timestamps, the following can be used:

result = pipe(sample, return_timestamps=True, generate_kwargs={"language": "french", "task": "translate"})
print(result["chunks"])

<details>

<summary> For more control over the generation parameters, use the model + processor API directly: </summary>

import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from datasets import Audio, load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "openai/whisper-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate))
sample = dataset[0]["audio"]
inputs = processor(
    sample["array"],
    sampling_rate=sample["sampling_rate"],
    return_tensors="pt",
    truncation=False,
    padding="longest",
    return_attention_mask=True,
)
inputs = inputs.to(device, dtype=torch_dtype)
gen_kwargs = {
    "max_new_tokens": 448,
    "num_beams": 1,
    "condition_on_prev_tokens": False,
    "compression_ratio_threshold": 1.35,  # zlib compression ratio threshold (in token space)
    "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
    "logprob_threshold": -1.0,
    "no_speech_threshold": 0.6,
    "return_timestamps": True,
}
pred_ids = model.generate(inputs, gen_kwargs)
pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=False)
print(pred_text)

</details>

Additional Speed & Memory Improvements

You can apply additional speed and memory improvements to Whisper to further reduce the inference speed and VRAM
requirements.

Chunked Long-Form

Whisper has a receptive field of 30-seconds. To transcribe audios longer than this, one of two long-form algorithms are
required:
1. *