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Parent(s):
f08ecef
Update app.py
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app.py
CHANGED
@@ -1,16 +1,18 @@
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import torch
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import gradio as gr
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import yt_dlp as youtube_dl
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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 tempfile
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import os
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import time
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# Model and setup
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MODEL_NAME = "openai/whisper-large-v3"
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BATCH_SIZE = 8
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device = 0 if torch.cuda.is_available() else "cpu"
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pipe = pipeline(
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device=device,
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)
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def transcribe(inputs, task):
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if inputs is None:
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raise gr.Error("No audio file submitted! Please upload or record an audio file.")
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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return
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# YouTube video processing functions
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def _return_yt_html_embed(yt_url):
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video_id = yt_url.split("?v=")[-1]
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def download_yt_audio(yt_url, filename):
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html_embed_str = _return_yt_html_embed(yt_url)
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with tempfile.TemporaryDirectory() as tmpdirname:
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filepath = os.path.join(tmpdirname, "video.mp4")
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download_yt_audio(yt_url, filepath)
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with open(filepath, "rb") as f:
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inputs = f.read()
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inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
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inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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return html_embed_str, text
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mf_transcribe = gr.Interface(
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fn=transcribe,
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inputs=[
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@@ -58,7 +100,12 @@ mf_transcribe = gr.Interface(
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layout="horizontal",
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theme="huggingface",
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title="Whisper Large V3: Transcribe Audio",
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description=
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)
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file_transcribe = gr.Interface(
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],
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outputs="text",
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layout="horizontal",
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theme="
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title="Whisper Large V3: Transcribe Audio",
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description=
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)
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yt_transcribe = gr.Interface(
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],
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outputs=["html", "text"],
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layout="horizontal",
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theme="
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title="Whisper Large V3: Transcribe YouTube",
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description=
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)
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with gr.Blocks(theme="NoCrypt/miku@1.2.1") as demo:
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gr.HTML("<h1><center>AI Assistant<h1><center>")
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gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
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demo.launch(enable_queue=True)
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import torch
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import gradio as gr
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import yt_dlp as youtube_dl
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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 tempfile
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import os
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MODEL_NAME = "openai/whisper-large-v3"
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BATCH_SIZE = 8
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FILE_LIMIT_MB = 1000
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YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files
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device = 0 if torch.cuda.is_available() else "cpu"
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pipe = pipeline(
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device=device,
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)
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def transcribe(inputs, task):
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if inputs is None:
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raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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return text
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def _return_yt_html_embed(yt_url):
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video_id = yt_url.split("?v=")[-1]
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HTML_str = (
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f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
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" </center>"
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)
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return HTML_str
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def download_yt_audio(yt_url, filename):
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info_loader = youtube_dl.YoutubeDL()
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try:
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info = info_loader.extract_info(yt_url, download=False)
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except youtube_dl.utils.DownloadError as err:
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raise gr.Error(str(err))
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file_length = info["duration_string"]
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file_h_m_s = file_length.split(":")
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file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
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if len(file_h_m_s) == 1:
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file_h_m_s.insert(0, 0)
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if len(file_h_m_s) == 2:
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file_h_m_s.insert(0, 0)
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file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
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if file_length_s > YT_LENGTH_LIMIT_S:
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yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
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file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
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raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
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ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
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with youtube_dl.YoutubeDL(ydl_opts) as ydl:
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try:
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ydl.download([yt_url])
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except youtube_dl.utils.ExtractorError as err:
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raise gr.Error(str(err))
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def yt_transcribe(yt_url, task, max_filesize=75.0):
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html_embed_str = _return_yt_html_embed(yt_url)
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with tempfile.TemporaryDirectory() as tmpdirname:
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filepath = os.path.join(tmpdirname, "video.mp4")
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download_yt_audio(yt_url, filepath)
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with open(filepath, "rb") as f:
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inputs = f.read()
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inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
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inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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return html_embed_str, text
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demo = gr.Blocks()
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mf_transcribe = gr.Interface(
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fn=transcribe,
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inputs=[
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layout="horizontal",
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theme="huggingface",
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title="Whisper Large V3: 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 🤗 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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file_transcribe = gr.Interface(
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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 Large V3: 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 🤗 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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yt_transcribe = gr.Interface(
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],
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outputs=["html", "text"],
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layout="horizontal",
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theme="huggingface",
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title="Whisper Large V3: Transcribe YouTube",
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description=(
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"Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
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f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
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" arbitrary length."
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),
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allow_flagging="never",
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)
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with demo:
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gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
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demo.launch(enable_queue=True)
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