Distil-Whisper is a distilled variant of the Whisper model by OpenAI. Compared to Whisper, Distil-Whisper runs 6x faster with 50% fewer parameters, while performing to within 1% word error rate (WER) on out-of-distribution evaluation data.
In this demo, we perform a speed comparison between Whisper and Distil-Whisper in order to test this claim. Both models use the chunked long-form transcription algorithm in 🤗 Transformers, as well as Flash Attention. To use Distil-Whisper yourself, check the code examples on the Distil-Whisper repository. 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"], ["./assets/example_2.wav"]], audio, outputs=[distil_transcription, distil_runtime, transcription, runtime], fn=transcribe, cache_examples=False, ) demo.queue(max_size=10).launch() 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( "models/kanyekuthi/AfriSpeech-whisper-tiny", 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( "models/kanyekuthi/AfriSpeech-whisper-tiny", 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("models/kanyekuthi/AfriSpeech-whisper-tiny") 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"}, return_timestamps=True ) 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( """Distil-Whisper is a distilled variant of the Whisper model by OpenAI. Compared to Whisper, Distil-Whisper runs 6x faster with 50% fewer parameters, while performing to within 1% word error rate (WER) on out-of-distribution evaluation data.
In this demo, we perform a speed comparison between Whisper and Distil-Whisper in order to test this claim. Both models use the chunked long-form transcription algorithm in 🤗 Transformers, as well as Flash Attention. To use Distil-Whisper yourself, check the code examples on the Distil-Whisper repository. 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"], ["./assets/example_2.wav"]], audio, outputs=[distil_transcription, distil_runtime, transcription, runtime], fn=transcribe, cache_examples=False, ) demo.queue(max_size=10).launch()