Create app.py

app.py

@@ -0,0 +1,88 @@
+import os
+import gradio as gr
+import whisper
+from whisper import tokenizer
+import time
+
+current_size = 'base'
+model = whisper.load_model(current_size)
+AUTO_DETECT_LANG = "Auto Detect"
+
+def transcribe(audio, state={}, model_size='base', delay=1.2, lang=None, translate=False):
+    time.sleep(delay - 1)
+
+    global current_size
+    global model
+    if model_size != current_size:
+        current_size = model_size
+        model = whisper.load_model(current_size)
+  
+    transcription = model.transcribe(
+        audio,
+        language = lang if lang != AUTO_DETECT_LANG else None
+    )
+    state['transcription'] += transcription['text'] + " "
+
+    if translate:
+        x = whisper.load_audio(audio)
+        x = whisper.pad_or_trim(x)
+        mel = whisper.log_mel_spectrogram(x).to(model.device)
+
+        options = whisper.DecodingOptions(task = "translation")
+        translation = whisper.decode(model, mel, options)
+
+        state['translation'] += translation.text + " "
+
+    return state['transcription'], state['translation'], state, f"detected language: {transcription['language']}"
+  
+
+title = "OpenAI's Whisper Real-time Demo"
+description = "A simple demo of OpenAI's [**Whisper**](https://github.com/openai/whisper) speech recognition model. This demo runs on a CPU. For faster inference choose 'tiny' model size and set the language explicitly."
+
+model_size = gr.Dropdown(label="Model size", choices=['base', 'tiny', 'small', 'medium', 'large'], value='base')
+
+delay_slider = gr.inputs.Slider(minimum=1, maximum=5, default=1.2, label="Rate of transcription")
+
+available_languages = sorted(tokenizer.TO_LANGUAGE_CODE.keys())
+available_languages = [lang.capitalize() for lang in available_languages]
+available_languages = [AUTO_DETECT_LANG]+available_languages
+
+lang_dropdown = gr.inputs.Dropdown(choices=available_languages, label="Language", default=AUTO_DETECT_LANG, type="value")
+
+if lang_dropdown==AUTO_DETECT_LANG:
+    lang_dropdown=None
+
+translate_checkbox = gr.inputs.Checkbox(label="Translate to English", default=False)
+
+
+
+transcription_tb = gr.Textbox(label="Transcription", lines=10, max_lines=20)
+translation_tb = gr.Textbox(label="Translation", lines=10, max_lines=20)
+detected_lang = gr.outputs.HTML(label="Detected Language")
+
+state = gr.State({"transcription": "", "translation": ""})
+
+gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type="filepath", streaming=True),
+        state,
+        model_size,
+        delay_slider,
+        lang_dropdown,
+        translate_checkbox
+        ], 
+    outputs=[
+        transcription_tb,
+        translation_tb,
+        state,
+        detected_lang
+    ],
+    live=True,
+    allow_flagging='never',
+    title=title,
+    description=description,
+).launch(
+    # enable_queue=True,
+    # debug=True
+  )