Upload app.py

app.py

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+from io import StringIO
+import gradio as gr
+
+from utils import write_vtt
+import whisper
+
+import ffmpeg
+
+#import os
+#os.system("pip install git+https://github.com/openai/whisper.git")
+
+# Limitations (set to -1 to disable)
+DEFAULT_INPUT_AUDIO_MAX_DURATION = 120 # seconds
+
+LANGUAGES = [ 
+ "English", "Chinese", "German", "Spanish", "Russian", "Korean", 
+ "French", "Japanese", "Portuguese", "Turkish", "Polish", "Catalan", 
+ "Dutch", "Arabic", "Swedish", "Italian", "Indonesian", "Hindi", 
+ "Finnish", "Vietnamese", "Hebrew", "Ukrainian", "Greek", "Malay", 
+ "Czech", "Romanian", "Danish", "Hungarian", "Tamil", "Norwegian", 
+ "Thai", "Urdu", "Croatian", "Bulgarian", "Lithuanian", "Latin", 
+ "Maori", "Malayalam", "Welsh", "Slovak", "Telugu", "Persian", 
+ "Latvian", "Bengali", "Serbian", "Azerbaijani", "Slovenian", 
+ "Kannada", "Estonian", "Macedonian", "Breton", "Basque", "Icelandic", 
+ "Armenian", "Nepali", "Mongolian", "Bosnian", "Kazakh", "Albanian",
+ "Swahili", "Galician", "Marathi", "Punjabi", "Sinhala", "Khmer", 
+ "Shona", "Yoruba", "Somali", "Afrikaans", "Occitan", "Georgian", 
+ "Belarusian", "Tajik", "Sindhi", "Gujarati", "Amharic", "Yiddish", 
+ "Lao", "Uzbek", "Faroese", "Haitian Creole", "Pashto", "Turkmen", 
+ "Nynorsk", "Maltese", "Sanskrit", "Luxembourgish", "Myanmar", "Tibetan",
+ "Tagalog", "Malagasy", "Assamese", "Tatar", "Hawaiian", "Lingala", 
+ "Hausa", "Bashkir", "Javanese", "Sundanese"
+]
+
+model_cache = dict()
+
+class UI:
+    def __init__(self, inputAudioMaxDuration):
+        self.inputAudioMaxDuration = inputAudioMaxDuration
+
+    def transcribeFile(self, modelName, languageName, uploadFile, microphoneData, task):
+        source = uploadFile if uploadFile is not None else microphoneData
+        selectedLanguage = languageName.lower() if len(languageName) > 0 else None
+        selectedModel = modelName if modelName is not None else "base"
+
+        if self.inputAudioMaxDuration > 0:
+            # Calculate audio length
+            audioDuration = ffmpeg.probe(source)["format"]["duration"]
+            
+            if float(audioDuration) > self.inputAudioMaxDuration:
+                return ("[ERROR]: Maximum audio file length is " + str(self.inputAudioMaxDuration) + "s, file was " + str(audioDuration) + "s"), "[ERROR]"
+
+        model = model_cache.get(selectedModel, None)
+        
+        if not model:
+            model = whisper.load_model(selectedModel)
+            model_cache[selectedModel] = model
+
+        result = model.transcribe(source, language=selectedLanguage, task=task)
+
+        segmentStream = StringIO()
+        write_vtt(result["segments"], file=segmentStream)
+        segmentStream.seek(0)
+
+        return result["text"], segmentStream.read()
+
+
+def createUi(inputAudioMaxDuration, share=False):
+    ui = UI(inputAudioMaxDuration)
+
+    ui_description = "Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse " 
+    ui_description += " audio and is also a multi-task model that can perform multilingual speech recognition "
+    ui_description += " as well as speech translation and language identification. "
+
+    if inputAudioMaxDuration > 0:
+        ui_description += "\n\n" + "Max audio file length: " + str(inputAudioMaxDuration) + " s"
+
+    demo = gr.Interface(fn=ui.transcribeFile, description=ui_description, inputs=[
+        gr.Dropdown(choices=["tiny", "base", "small", "medium", "large"], value="medium", label="Model"),
+        gr.Dropdown(choices=sorted(LANGUAGES), label="Language"),
+        gr.Audio(source="upload", type="filepath", label="Upload Audio"), 
+        gr.Audio(source="microphone", type="filepath", label="Microphone Input"),
+        gr.Dropdown(choices=["transcribe", "translate"], label="Task"),
+    ], outputs=[gr.Text(label="Transcription"), gr.Text(label="Segments")])
+
+    demo.launch(share=share)   
+
+if __name__ == '__main__':
+    createUi(DEFAULT_INPUT_AUDIO_MAX_DURATION)