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Update app.py
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app.py
CHANGED
@@ -1,25 +1,49 @@
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import gradio as gr
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import whisper
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from pytube import YouTube
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class GradioInference():
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def __init__(self):
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self.sizes = list(whisper._MODELS.keys())
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self.current_size = "base"
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self.loaded_model = whisper.load_model(self.current_size)
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self.yt = None
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def __call__(self, link,
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if self.yt is None:
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self.yt = YouTube(link)
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-
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path = self.yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")
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if size != self.current_size:
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self.loaded_model = whisper.load_model(size)
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self.current_size = size
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results = self.loaded_model.transcribe(path)
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def format_time(self, time):
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hours = time//3600
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@@ -37,7 +61,6 @@ title="Youtube Whisperer"
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description="Speech to text transcription of Youtube videos using OpenAI's Whisper"
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block = gr.Blocks()
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with block:
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gr.HTML(
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"""
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with gr.Box():
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with gr.Row().style(equal_height=True):
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sz = gr.Dropdown(label="Model Size", choices=gio.sizes, value='base')
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link = gr.Textbox(label="YouTube Link")
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title = gr.Label(label="Video Title")
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with gr.Row().style(equal_height=True):
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text = gr.Textbox(label="Transcription", placeholder="Transcription Output", lines=10)
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with gr.Row().style(equal_height=True):
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btn = gr.Button("Transcribe")
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btn.click(gio, inputs=[link, sz], outputs=[text])
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link.change(gio.populate_metadata, inputs=[link], outputs=[img, title])
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block.launch()
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import gradio as gr
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import whisper
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from pytube import YouTube
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class GradioInference():
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def __init__(self):
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self.sizes = list(whisper._MODELS.keys())
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self.langs = ["none"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
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self.current_size = "base"
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self.loaded_model = whisper.load_model(self.current_size)
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self.yt = None
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def __call__(self, link, lang, size, subs):
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if self.yt is None:
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self.yt = YouTube(link)
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path = self.yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")
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if lang == "none":
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lang = None
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if size != self.current_size:
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self.loaded_model = whisper.load_model(size)
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self.current_size = size
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results = self.loaded_model.transcribe(path, language=lang)
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if subs == "None":
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return results["text"]
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elif subs == ".srt":
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return self.srt(results["segments"])
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elif ".csv" == ".csv":
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return self.csv(results["segments"])
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def srt(self, segments):
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output = ""
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for i, segment in enumerate(segments):
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output += f"{i+1}\n"
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output += f"{self.format_time(segment['start'])} --> {self.format_time(segment['end'])}\n"
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output += f"{segment['text']}\n\n"
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return output
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def csv(self, segments):
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output = ""
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for segment in segments:
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output += f"{segment['start']},{segment['end']},{segment['text']}\n"
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return output
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def format_time(self, time):
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hours = time//3600
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description="Speech to text transcription of Youtube videos using OpenAI's Whisper"
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block = gr.Blocks()
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with block:
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gr.HTML(
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"""
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with gr.Box():
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with gr.Row().style(equal_height=True):
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sz = gr.Dropdown(label="Model Size", choices=gio.sizes, value='base')
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lang = gr.Dropdown(label="Language (Optional)", choices=gio.langs, value="none")
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with gr.Row().style(equal_height=True):
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wt = gr.Radio(["None", ".srt", ".csv"], label="With Timestamps?")
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link = gr.Textbox(label="YouTube Link")
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title = gr.Label(label="Video Title")
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with gr.Row().style(equal_height=True):
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text = gr.Textbox(label="Transcription", placeholder="Transcription Output", lines=10)
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with gr.Row().style(equal_height=True):
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btn = gr.Button("Transcribe")
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btn.click(gio, inputs=[link, lang, sz, wt], outputs=[text])
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link.change(gio.populate_metadata, inputs=[link], outputs=[img, title])
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block.launch()
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