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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( | |
"openai/whisper-large-v2", torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, use_flash_attention_2=use_flash_attention_2 | |
) | |
distilled_model = AutoModelForSpeechSeq2Seq.from_pretrained( | |
"distil-whisper/distil-large-v2", torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, use_flash_attention_2=use_flash_attention_2 | |
) | |
if not use_flash_attention_2: | |
# use flash attention from pytorch sdpa | |
model = model.to_bettertransformer() | |
distilled_model = distilled_model.to_bettertransformer() | |
processor = AutoProcessor.from_pretrained("openai/whisper-large-v2") | |
model.to(device) | |
distilled_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": "en", "task": "transcribe"}, | |
) | |
pipe_forward = pipe._forward | |
distil_pipe = pipeline( | |
"automatic-speech-recognition", | |
model=distilled_model, | |
tokenizer=processor.tokenizer, | |
feature_extractor=processor.feature_extractor, | |
max_new_tokens=128, | |
chunk_length_s=15, | |
torch_dtype=torch_dtype, | |
device=device, | |
generate_kwargs={"language": "en", "task": "transcribe"}, | |
) | |
distil_pipe_forward = distil_pipe._forward | |
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} | |
def _forward_distil_time(*args, **kwargs): | |
global distil_runtime | |
start_time = time.time() | |
result = distil_pipe_forward(*args, **kwargs) | |
distil_runtime = time.time() - start_time | |
distil_runtime = round(distil_runtime, 2) | |
return result | |
distil_pipe._forward = _forward_distil_time | |
distil_text = distil_pipe(inputs.copy(), batch_size=BATCH_SIZE)["text"] | |
yield distil_text, distil_runtime, None, None, None | |
def _forward_time(*args, **kwargs): | |
global runtime | |
start_time = time.time() | |
result = pipe_forward(*args, **kwargs) | |
runtime = time.time() - start_time | |
runtime = round(runtime, 2) | |
return result | |
pipe._forward = _forward_time | |
text = pipe(inputs, batch_size=BATCH_SIZE)["text"] | |
yield distil_text, distil_runtime, text, runtime | |
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;"> | |
Whisper vs Distil-Whisper: Speed Comparison | |
</h1> | |
</div> | |
</div> | |
""" | |
) | |
gr.HTML( | |
f""" | |
Speed comparison between <a href="https://huggingface.co/openai/whisper-large-v2"> Whisper</a> | |
and <a href="https://huggingface.co/distil-whisper/distil-large-v2"> Distil-Whisper</a>. Both models use the <a href="https://huggingface.co/distil-whisper/distil-large-v2#long-form-transcription"> chunked long-form transcription algorithm</a> | |
in 🤗 Transformers with Flash Attention support. To ensure fair usage of the Space, we ask that audio | |
file inputs are kept to < 30 mins. | |
""" | |
) | |
audio = gr.components.Audio(type="filepath", label="Audio input") | |
button = gr.Button("Transcribe") | |
with gr.Row(): | |
distil_runtime = gr.components.Textbox(label="Distil-Whisper Transcription Time (s)") | |
runtime = gr.components.Textbox(label="Whisper Transcription Time (s)") | |
with gr.Row(): | |
distil_transcription = gr.components.Textbox(label="Distil-Whisper Transcription", show_copy_button=True) | |
transcription = gr.components.Textbox(label="Whisper Transcription", show_copy_button=True) | |
button.click( | |
fn=transcribe, | |
inputs=audio, | |
outputs=[distil_transcription, distil_runtime, transcription, runtime], | |
) | |
gr.Markdown("## Examples") | |
gr.Examples( | |
[["./assets/example_1.wav"]], | |
audio, | |
outputs=[distil_transcription, distil_runtime, transcription, runtime], | |
fn=transcribe, | |
cache_examples=False, | |
) | |
demo.queue(max_size=10).launch() | |