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| import torch | |
| from transformers import pipeline | |
| from transformers.pipelines.audio_utils import ffmpeg_read | |
| import gradio as gr | |
| MODEL_NAME = "openai/whisper-small" | |
| BATCH_SIZE = 8 | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=MODEL_NAME, | |
| chunk_length_s=30, | |
| device=device, | |
| ) | |
| # Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50 | |
| def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."): | |
| if seconds is not None: | |
| milliseconds = round(seconds * 1000.0) | |
| hours = milliseconds // 3_600_000 | |
| milliseconds -= hours * 3_600_000 | |
| minutes = milliseconds // 60_000 | |
| milliseconds -= minutes * 60_000 | |
| seconds = milliseconds // 1_000 | |
| milliseconds -= seconds * 1_000 | |
| hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" | |
| return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" | |
| else: | |
| # we have a malformed timestamp so just return it as is | |
| return seconds | |
| def transcribe(file, task, return_timestamps): | |
| outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=return_timestamps) | |
| text = outputs["text"] | |
| if return_timestamps: | |
| timestamps = outputs["chunks"] | |
| timestamps = [ | |
| f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}" | |
| for chunk in timestamps | |
| ] | |
| text = "\n".join(str(feature) for feature in timestamps) | |
| return text | |
| demo = gr.Blocks() | |
| mic_transcribe = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type="filepath", optional=True), | |
| gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), | |
| gr.inputs.Checkbox(default=False, label="Return timestamps"), | |
| ], | |
| outputs="text", | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper Demo: Transcribe Audio", | |
| description=( | |
| "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the" | |
| f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" | |
| " of arbitrary length." | |
| ), | |
| allow_flagging="never", | |
| ) | |
| file_transcribe = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"), | |
| gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), | |
| gr.inputs.Checkbox(default=False, label="Return timestamps"), | |
| ], | |
| outputs="text", | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper Demo: Transcribe Audio", | |
| description=( | |
| "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the" | |
| f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" | |
| " of arbitrary length." | |
| ), | |
| examples=[ | |
| ["./example.flac", "transcribe", False], | |
| ["./example.flac", "transcribe", True], | |
| ], | |
| cache_examples=True, | |
| allow_flagging="never", | |
| ) | |
| with demo: | |
| gr.TabbedInterface([mic_transcribe, file_transcribe], ["Transcribe Microphone", "Transcribe Audio File"]) | |
| demo.launch(enable_queue=True) |