Update app.py

app.py

@@ -4,56 +4,55 @@ import whisper
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 from gtts import gTTS
 
-def translate_voice(file_obj, target_lang):
-    # Save the temporary file to disk
-    temp_file_path = "temp_audio_file.wav"
-    with open(temp_file_path, "wb") as out_file:
-        out_file.write(file_obj.read())
+def translate_voice(file, target_lang):
+    try:
+        # Load the model and switch to float32
+        model = whisper.load_model("base").float()
 
-    # Load the model and switch to float32
-    model = whisper.load_model("base").float()
+        # Load the audio
+        audio = whisper.load_audio(file.name)
 
-    # Load the audio
-    audio = whisper.load_audio(temp_file_path)
+        # Pad or trim the audio
+        audio = whisper.pad_or_trim(audio)
 
-    # Pad or trim the audio
-    audio = whisper.pad_or_trim(audio)
+        # Convert the audio to a log Mel spectrogram and move it to the same device as the model (CPU in your case)
+        mel = whisper.log_mel_spectrogram(audio).to(model.device).float()  # convert to full-precision float32
 
-    # Convert the audio to a log Mel spectrogram and move it to the same device as the model (CPU in your case)
-    mel = whisper.log_mel_spectrogram(audio).to(model.device).float()  # convert to full-precision float32
+        # Proceed with your language detection and decoding
+        _, probs = model.detect_language(mel)
+        options = whisper.DecodingOptions()
+        result = whisper.decode(model, mel, options)
 
-    # Proceed with your language detection and decoding
-    _, probs = model.detect_language(mel)
-    options = whisper.DecodingOptions()
-    result = whisper.decode(model, mel, options)
+        text = result.text
+        lang = max(probs, key=probs.get)
 
-    text = result.text
-    lang = max(probs, key=probs.get)
+        # Translate
+        tokenizer = AutoTokenizer.from_pretrained("alirezamsh/small100")
+        model = AutoModelForSeq2SeqLM.from_pretrained("alirezamsh/small100")
 
-    # Translate
-    tokenizer = AutoTokenizer.from_pretrained("alirezamsh/small100")
-    model = AutoModelForSeq2SeqLM.from_pretrained("alirezamsh/small100")
+        tokenizer.src_lang = target_lang
+        encoded_bg = tokenizer(text, return_tensors="pt")
+        generated_tokens = model.generate(**encoded_bg)
+        translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
 
-    tokenizer.src_lang = target_lang
-    encoded_bg = tokenizer(text, return_tensors="pt")
-    generated_tokens = model.generate(**encoded_bg)
-    translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+        # Text-to-audio (TTS)
+        tts = gTTS(text=translated_text, lang=target_lang)
+        filename = "to_speech.mp3"
+        tts.save(filename)
 
-    # Text-to-audio (TTS)
-    tts = gTTS(text=translated_text, lang=target_lang)
-    filename = "to_speech.mp3"
-    tts.save(filename)
+        return filename, text, translated_text, target_lang
 
-    return filename, text, translated_text, target_lang
+    except Exception as e:
+        return str(e), "", "", ""
 
 iface = gr.Interface(
     fn=translate_voice, 
     inputs=[
-        gr.inputs.File(type="file", label="Your Audio"), 
+        gr.inputs.File(label="Your Audio"), 
         gr.inputs.Dropdown(choices=['en', 'ru', 'de', 'fr'], label="Target Language")
     ], 
     outputs=[
-        gr.outputs.Audio(type="filepath", label="Translated Audio"),
+        gr.outputs.Audio(type="auto", label="Translated Audio"),
         gr.outputs.Textbox(label="Original Text"),
         gr.outputs.Textbox(label="Translated Text"),
         gr.outputs.Textbox(label="Target Language"),