README.md

metadata

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
license: apache-2.0
tags:
  - whisper
  - speech-recognition
  - multilingual
  - automatic-speech-recognition
pipeline_tag: automatic-speech-recognition
library_name: transformers
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

MultilingualSTT

OpenAI's Whisper Large V3 model for multilingual speech-to-text transcription.

Model Description

Whisper Large V3 is a state-of-the-art automatic speech recognition (ASR) model that supports 99+ languages. It provides highly accurate transcription across a wide range of languages and acoustic conditions.

Key Features

• 99+ Languages: Supports English, Chinese, German, Spanish, Russian, Korean, French, Japanese, Portuguese, Turkish, Polish, Italian, Hindi, Arabic, and many more
• Speech Translation: Can translate speech to English
• Timestamps: Supports word-level and sentence-level timestamps
• Robust: Excellent handling of accents, background noise, and technical language

Usage

import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "Svetozar1993/MultilingualSTT"

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,
)

result = pipe("audio.mp3")
print(result["text"])

Advanced Usage

Specify Language

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

Speech Translation (to English)

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

Get Timestamps

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

Word-Level Timestamps

result = pipe(sample, return_timestamps="word")

Flash Attention 2

For faster inference:

pip install flash-attn --no-build-isolation

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id,
    torch_dtype=torch_dtype,
    low_cpu_mem_usage=True,
    attn_implementation="flash_attention_2"
)

Model Details

• Architecture: Whisper Large V3
• Parameters: 1.55B
• Languages: 99+
• License: Apache 2.0

Author

Svetozar1993