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Duplicate from jason1i/whisper-small

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  1. .gitattributes +34 -0
  2. README.md +13 -0
  3. app.py +101 -0
  4. example.flac +0 -0
  5. packages.txt +1 -0
  6. requirements.txt +3 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ title: Whisper Small
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+ emoji: 🌍
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+ colorFrom: pink
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+ colorTo: pink
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+ sdk: gradio
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+ sdk_version: 3.29.0
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+ app_file: app.py
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+ pinned: false
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+ duplicated_from: jason1i/whisper-small
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ import torch
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+ from transformers import pipeline
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+ from transformers.pipelines.audio_utils import ffmpeg_read
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+ import gradio as gr
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+
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+ MODEL_NAME = "openai/whisper-small"
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+ BATCH_SIZE = 8
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+
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+ device = 0 if torch.cuda.is_available() else "cpu"
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+
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+ pipe = pipeline(
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+ task="automatic-speech-recognition",
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+ model=MODEL_NAME,
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+ chunk_length_s=30,
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+ device=device,
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+ )
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+
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+
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+ # Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50
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+ def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
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+ if seconds is not None:
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+ milliseconds = round(seconds * 1000.0)
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+
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+ hours = milliseconds // 3_600_000
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+ milliseconds -= hours * 3_600_000
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+
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+ minutes = milliseconds // 60_000
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+ milliseconds -= minutes * 60_000
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+
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+ seconds = milliseconds // 1_000
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+ milliseconds -= seconds * 1_000
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+
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+ hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
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+ return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
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+ else:
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+ # we have a malformed timestamp so just return it as is
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+ return seconds
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+
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+
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+ def transcribe(file, task, return_timestamps):
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+ outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=return_timestamps)
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+ text = outputs["text"]
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+ if return_timestamps:
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+ timestamps = outputs["chunks"]
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+ timestamps = [
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+ f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
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+ for chunk in timestamps
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+ ]
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+ text = "\n".join(str(feature) for feature in timestamps)
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+ return text
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+
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+
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+ demo = gr.Blocks()
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+
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+ mic_transcribe = gr.Interface(
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+ fn=transcribe,
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+ inputs=[
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+ gr.inputs.Audio(source="microphone", type="filepath", optional=True),
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+ gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
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+ gr.inputs.Checkbox(default=False, label="Return timestamps"),
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+ ],
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+ outputs="text",
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+ layout="horizontal",
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+ theme="huggingface",
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+ title="Whisper Demo: Transcribe Audio",
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+ description=(
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+ "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
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+ f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and πŸ€— Transformers to transcribe audio files"
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+ " of arbitrary length."
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+ ),
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+ allow_flagging="never",
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+ )
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+
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+ file_transcribe = gr.Interface(
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+ fn=transcribe,
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+ inputs=[
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+ gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"),
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+ gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
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+ gr.inputs.Checkbox(default=False, label="Return timestamps"),
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+ ],
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+ outputs="text",
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+ layout="horizontal",
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+ theme="huggingface",
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+ title="Whisper Demo: Transcribe Audio",
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+ description=(
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+ "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
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+ f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and πŸ€— Transformers to transcribe audio files"
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+ " of arbitrary length."
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+ ),
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+ examples=[
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+ ["./example.flac", "transcribe", False],
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+ ["./example.flac", "transcribe", True],
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+ ],
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+ cache_examples=True,
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+ allow_flagging="never",
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+ )
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+
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+ with demo:
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+ gr.TabbedInterface([mic_transcribe, file_transcribe], ["Transcribe Microphone", "Transcribe Audio File"])
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+
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+ demo.launch(enable_queue=True)
example.flac ADDED
Binary file (282 kB). View file
 
packages.txt ADDED
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+ ffmpeg
requirements.txt ADDED
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+ --extra-index-url https://download.pytorch.org/whl/cu113
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+ torch
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+ transformers