Create app.py

app.py

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+import torch
+from transformers import pipeline
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+pipe = pipeline(
+    "automatic-speech-recognition", model="openai/whisper-base", device=device
+)
+
+from datasets import load_dataset
+
+dataset = load_dataset("facebook/voxpopuli", "en", split="validation", streaming=True, trust_remote_code=True)  
+sample = next(iter(dataset))
+
+def translate(audio):
+    outputs = pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "fr"}) # "language": "fr"
+    return outputs["text"]
+
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+
+model = SpeechT5ForTextToSpeech.from_pretrained("ccourc23/fine_tuned_SpeechT5") 
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+def synthesise(text):
+    inputs = processor(text=text, return_tensors="pt")
+    speech = model.generate_speech(
+        inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder
+    )
+    return speech.cpu()
+
+Audio(speech, rate=16000)
+
+import numpy as np
+
+target_dtype = np.int16
+max_range = np.iinfo(target_dtype).max
+
+
+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
+
+import gradio as gr
+
+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(debug=True, share=True)