Delete app.py

app.py

@@ -1,57 +0,0 @@
-import torch
-from transformers import pipeline
-from transformers import VitsModel, VitsTokenizer
-import numpy as np
-import gradio as gr
-
-target_dtype = np.int16
-max_range = np.iinfo(target_dtype).max
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model="openai/whisper-base",
-    device=device
-)
-
-def translate(audio):
-    outputs = pipe(
-        audio,
-        max_new_tokens=256,
-        generate_kwargs={"task": "transcribe", "language": "es"}
-    )
-
-model = VitsModel.from_pretrained("facebook/mms-tts-spa")
-tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-spa")
-
-def synthesise(text):
-    inputs=tokenizer(text, return_tensors="pt")
-    input_ids = inputs["input_ids"]
-    with torch.no_grad():
-        outputs = model(input_ids)
-    return outputs["waveform"]
-
-def speech_to_speech_translation(audio):
-    translated_text = translate(audio)
-    synthesised_speech = synthesise(translated_text)
-    synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
-    return 16000, synthesised_speech
-
-demo = gr.Blocks()
-
-mic_translate = gr.Interface(
-    fn=speech_to_speech_translation,
-    inputs=gr.Audio(sources="microphone", type="filepath"),
-    outputs=gr.Audio(label="Generated Speech", type="numpy"),
-)
-
-file_translate = gr.Interface(
-    fn=speech_to_speech_translation,
-    inputs=gr.Audio(sources="upload", type="filepath"),
-    outputs=gr.Audio(label="Generated Speech", type="numpy"),
-)
-
-with demo:
-    gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
-
-demo.launch()