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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline | |
from transformers.utils import is_flash_attn_2_available | |
from transformers.pipelines.audio_utils import ffmpeg_read | |
import torch | |
import gradio as gr | |
import time | |
BATCH_SIZE = 16 | |
MAX_AUDIO_MINS = 30 # maximum audio input in minutes | |
device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 | |
use_flash_attention_2 = is_flash_attn_2_available() | |
model = AutoModelForSpeechSeq2Seq.from_pretrained( | |
"primeline/whisper-large-v3-german", torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=False, use_flash_attention_2=use_flash_attention_2 | |
) | |
if not use_flash_attention_2: | |
# use flash attention from pytorch sdpa | |
model = model.to_bettertransformer() | |
processor = AutoProcessor.from_pretrained("primeline/whisper-large-v3-german") | |
model.to(device) | |
pipe = pipeline( | |
"automatic-speech-recognition", | |
model=model, | |
tokenizer=processor.tokenizer, | |
feature_extractor=processor.feature_extractor, | |
max_new_tokens=128, | |
chunk_length_s=30, | |
torch_dtype=torch_dtype, | |
device=device, | |
generate_kwargs={"language": "de", "task": "transcribe"}, | |
return_timestamps=True | |
) | |
def transcribe(inputs): | |
if inputs is None: | |
raise gr.Error("No audio file submitted! Please record or upload an audio file before submitting your request.") | |
with open(inputs, "rb") as f: | |
inputs = f.read() | |
inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate) | |
audio_length_mins = len(inputs) / pipe.feature_extractor.sampling_rate / 60 | |
if audio_length_mins > MAX_AUDIO_MINS: | |
raise gr.Error( | |
f"To ensure fair usage of the Space, the maximum audio length permitted is {MAX_AUDIO_MINS} minutes." | |
f"Got an audio of length {round(audio_length_mins, 3)} minutes." | |
) | |
inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate} | |
text = pipe(inputs, batch_size=BATCH_SIZE)["text"] | |
yield text | |
if __name__ == "__main__": | |
with gr.Blocks() as demo: | |
gr.HTML( | |
""" | |
<div style="text-align: center; max-width: 700px; margin: 0 auto;"> | |
<div | |
style=" | |
display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem; | |
" | |
> | |
<h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;"> | |
KI Spracherkennung: Kannst du schnell genug reden damit Whisper-German dich <u>nicht</u> versteht? | |
</h1> | |
</div> | |
</div> | |
""" | |
) | |
gr.HTML( | |
f""" | |
<p><a href="https://huggingface.co/primeline/whisper-large-v3-german"> Whisper-German</a> ist eines der besten Deutschen | |
Spracherkennungs Modelle die es gibt. Es basiert auf OpenAI's <a href="https://huggingface.co/openai/whisper-large-v3"> Whisper-v3</a> und wurde auf qualitativ | |
hochwertigen deutschen Audio Daten weiter trainert </p> | |
<p> Um zu demonstrieren wie <strong>gut</strong> das Model ist, laden wir dich ein zu versuchen es zu Fehlern zu zwingen. Rede so schnell wie du kannst, so unverstaendlich wie | |
du kannst oder benutze moglichst komplizierte Wörter um das Modelle dazu zu bringen falsche Transkriptionen zu generieren. | |
<strong> Diese Demo speichert keinerlei Daten von dir </strong>. | |
</p> | |
""" | |
) | |
audio = gr.components.Audio(type="filepath", label="Audio input", sources="microphone") | |
button = gr.Button("Transkribiere") | |
with gr.Row(): | |
transcription = gr.components.Textbox(label="Whisper-German Transkription", show_copy_button=True) | |
button.click( | |
fn=transcribe, | |
inputs=audio, | |
outputs=[transcription], | |
) | |
demo.queue(max_size=10).launch() | |