Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+import soundfile as sf
+import numpy as np
+import scipy.io.wavfile as wavfile
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+device = 'cpu'
+print("Device:", device)
+
+pipe_translate = pipeline("translation", model="Helsinki-NLP/opus-mt-fr-en", device=device)
+pipe_tts = pipeline("text-to-speech", model="facebook/mms-tts-eng", device=device) # Better quality, way faster than bark
+
+def get_translation(text):
+    return pipe_translate(text)[0]["translation_text"]
+
+def get_audio(text):
+    speech = pipe_tts(text)
+    return speech["sampling_rate"], (speech["audio"]* 32767).astype(np.int16).T
+
+with gr.Blocks() as demo:    
+    input_text = gr.Textbox(
+                label="Input text",
+                info="Your text",
+                lines=3,
+                placeholder="Écrire le texte à traduire",
+            )
+
+    
+    
+    translation_button = gr.Button("Traduire...")
+    output_text = gr.Textbox(
+                label="Output text",
+                info="Your text",
+                lines=3,
+                placeholder="Votre traduction",
+            )
+    speech_button = gr.Button("Générer audio...")
+    translation_button.click(
+            get_translation,
+            inputs=[
+                input_text
+            ],
+            outputs=[
+                output_text
+            ],
+    )
+    speech_button.click(
+            get_audio,
+            inputs=[
+                output_text
+            ],
+            outputs=[
+                gr.Audio(label="Output")
+            ],
+    )
+
+demo.launch()