Upload 2 files

app.py

@@ -0,0 +1,93 @@
+import gradio as gr
+import whisper
+from pytube import YouTube
+
+
+class GradioInference():
+  def __init__(self):
+    self.sizes = list(whisper._MODELS.keys())
+    self.langs = ["none"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
+    self.current_size = "base"
+    self.loaded_model = whisper.load_model(self.current_size)
+    self.yt = None
+  
+  def __call__(self, link, lang, size, subs):
+    if self.yt is None:
+      self.yt = YouTube(link)
+    path = self.yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")
+
+    if lang == "none":
+      lang = None
+
+    if size != self.current_size:
+      self.loaded_model = whisper.load_model(size)
+      self.current_size = size
+    results = self.loaded_model.transcribe(path, language=lang)
+
+    if subs == "None":
+      return results["text"]
+    elif subs == ".srt":
+      return self.srt(results["segments"])
+    elif ".csv" == ".csv":
+      return self.csv(results["segments"])
+   
+  def srt(self, segments):
+    output = ""
+    for i, segment in enumerate(segments):
+      output += f"{i+1}\n"
+      output += f"{self.format_time(segment['start'])} --> {self.format_time(segment['end'])}\n"
+      output += f"{segment['text']}\n\n"
+    return output
+  
+  def csv(self, segments):
+    output = ""
+    for segment in segments:
+      output += f"{segment['start']},{segment['end']},{segment['text']}\n"
+    return output
+
+  def format_time(self, time):
+    hours = time//3600
+    minutes = (time - hours*3600)//60
+    seconds = time - hours*3600 - minutes*60
+    milliseconds = (time - int(time))*1000
+    return f"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d},{int(milliseconds):03d}"
+    
+  def populate_metadata(self, link):
+    self.yt = YouTube(link)
+    return self.yt.thumbnail_url, self.yt.title
+
+gio = GradioInference()
+title="Youtube Whisperer"
+description="Speech to text transcription of Youtube videos using OpenAI's Whisper"
+
+block = gr.Blocks()
+with block:
+    gr.HTML(
+        """
+            <div style="text-align: center; max-width: 500px; margin: 0 auto;">
+              <div>
+                <h1>Youtube Whisperer</h1>
+              </div>
+              <p style="margin-bottom: 10px; font-size: 94%">
+                Speech to text transcription of Youtube videos using OpenAI's Whisper
+              </p>
+            </div>
+        """
+    )
+    with gr.Group():
+        with gr.Box():
+          with gr.Row().style(equal_height=True):
+            sz = gr.Dropdown(label="Model Size", choices=gio.sizes, value='base')
+            lang = gr.Dropdown(label="Language (Optional)", choices=gio.langs, value="none")
+          with gr.Row().style(equal_height=True):
+            wt = gr.Radio(["None", ".srt", ".csv"], label="With Timestamps?")
+          link = gr.Textbox(label="YouTube Link")
+          title = gr.Label(label="Video Title")
+          with gr.Row().style(equal_height=True):
+            img = gr.Image(label="Thumbnail")
+            text = gr.Textbox(label="Transcription", placeholder="Transcription Output", lines=10)
+          with gr.Row().style(equal_height=True): 
+              btn = gr.Button("Transcribe")       
+          btn.click(gio, inputs=[link, lang, sz, wt], outputs=[text])
+          link.change(gio.populate_metadata, inputs=[link], outputs=[img, title])
+block.launch()

requirements.txt

@@ -0,0 +1,3 @@
+transformers
+git+https://github.com/openai/whisper.git
+pytube