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turicas/
faster-whisper-large-v3

Name: faster-whisper-large-v3
Availability: InStock

transformers

apache-2.0

endpoints_compatible

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faster-whisper-large-v3

This is the model Whisper large-v3 converted to be used in faster-whisper.

Using

You can choose between monkey-patching faster-whisper 0.9.0 (while they don't update it) or using my fork (which is easier).

Using my fork

First, install it by executing:

pip install -U 'transformers[torch]>=4.35.0' https://github.com/PythonicCafe/faster-whisper/archive/refs/heads/feature/large-v3.zip#egg=faster-whisper

Then, use it as the regular faster-whisper:

python

import time

import faster_whisper


filename = "my-audio.mp3"
initial_prompt = "My podcast recording"  # Or `None`
word_timestamps = False
vad_filter = True
temperature = 0.0
language = "pt"
model_size = "large-v3"
device, compute_type = "cuda", "float16"
# or: device, compute_type = "cpu", "float32"

model = faster_whisper.WhisperModel(model_size, device=device, compute_type=compute_type)

segments, transcription_info = model.transcribe(
    filename,
    word_timestamps=word_timestamps,
    vad_filter=vad_filter,
    temperature=temperature,
    language=language,
    initial_prompt=initial_prompt,
)
print(transcription_info)

start_time = time.time()
for segment in segments:
    row = {
        "start": segment.start,
        "end": segment.end,
        "text": segment.text,
    }
    if word_timestamps:
        row["words"] = [
            {"start": word.start, "end": word.end, "word": word.word}
            for word in segment.words
        ]
    print(row)
end_time = time.time()
print(f"Transcription finished in {end_time - start_time:.2f}s")

Monkey-patching faster-whisper 0.9.0

Make sure you have the latest version:

pip install -U 'faster-whisper>=0.9.0'

Then, use it with some little changes:

python

import time

import faster_whisper.transcribe


# Monkey patch 1 (add model to list)
faster_whisper.utils._MODELS["large-v3"] = "turicas/faster-whisper-large-v3"

# Monkey patch 2 (fix Tokenizer)
faster_whisper.transcribe.Tokenizer.encode = lambda self, text: self.tokenizer.encode(text, add_special_tokens=False)

filename = "my-audio.mp3"
initial_prompt = "My podcast recording"  # Or `None`
word_timestamps = False
vad_filter = True
temperature = 0.0
language = "pt"
model_size = "large-v3"
device, compute_type = "cuda", "float16"
# or: device, compute_type = "cpu", "float32"

model = faster_whisper.transcribe.WhisperModel(model_size, device=device, compute_type=compute_type)

# Monkey patch 3 (change n_mels)
from faster_whisper.feature_extractor import FeatureExtractor
model.feature_extractor = FeatureExtractor(feature_size=128)

# Monkey patch 4 (change tokenizer)
from transformers import AutoProcessor
model.hf_tokenizer = AutoProcessor.from_pretrained("openai/whisper-large-v3").tokenizer
model.hf_tokenizer.token_to_id = lambda token: model.hf_tokenizer.convert_tokens_to_ids(token)

segments, transcription_info = model.transcribe(
    filename,
    word_timestamps=word_timestamps,
    vad_filter=vad_filter,
    temperature=temperature,
    language=language,
    initial_prompt=initial_prompt,
)
print(transcription_info)

start_time = time.time()
for segment in segments:
    row = {
        "start": segment.start,
        "end": segment.end,
        "text": segment.text,
    }
    if word_timestamps:
        row["words"] = [
            {"start": word.start, "end": word.end, "word": word.word}
            for word in segment.words
        ]
    print(row)
end_time = time.time()
print(f"Transcription finished in {end_time - start_time:.2f}s")

Converting

If you'd like to convert the model yourself, execute:

shell

pip install -U 'ctranslate2>=3.21.0' 'transformers-4.35.0' 'OpenNMT-py==2.*' sentencepiece
ct2-transformers-converter --model openai/whisper-large-v3 --output_dir whisper-large-v3-ct2

Then, the files will be at whisper-large-v3-ct2/.

License

These files have the same license as the original openai/whisper-large-v3 model: Apache 2.0.