Update app.py

app.py

@@ -1,5 +1,90 @@
 import gradio as gr
 import numpy as np
+import torch
+from datasets import load_dataset
+from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+title = "GenAI Audio Demo"
+description = """
+Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in English. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's
+[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech:
+![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation")
+"""
+
+# Load speech translation pipeline
+asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
+
+# Load text-to-speech processor from pretrained dataset
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
+
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+# Function for translating different language using pretrained models
+def translate(audio):
+    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
+    return outputs["text"]
+
+# Function to synthesise the text using the processor above
+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()
+
+# Main function
+def speech_to_speech_translation(audio):
+    translated_text = translate(audio)
+    synthesised_speech = synthesise(translated_text)
+    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
+    return 16000, synthesised_speech
+
+# Function for text to speech
+def text_to_speech(text):
+    synthesised_speech = synthesise(text)
+    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
+    return 16000, synthesised_speech
+
+demo = gr.Blocks()
+
+# Mic translation using microphone as the input
+mic_translate = gr.Interface(
+    fn=speech_to_speech_translation,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
+    title=title,
+    description=description,
+)
+
+# File translation using uploaded files as input
+file_translate = gr.Interface(
+    fn=speech_to_speech_translation,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
+    examples=[["./english.wav"], ["./chinese.wav"]],
+    title=title,
+    description=description,
+)
+
+ # Text translation using text as input
+text_translate = gr.Interface(
+    fn=text_to_speech, 
+    inputs="textbox",
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
+    title=title,
+    description=description
+)
+
+# Showcase the demo using different tabs of the different features
+with demo:
+    gr.TabbedInterface([mic_translate, file_translate, text_translate], ["Microphone", "Audio File", "Text to Speech"])
+
+demo.launch()
+'''import gradio as gr
+import numpy as np
 import random
 from diffusers import DiffusionPipeline
 import torch
@@ -143,4 +228,4 @@ with gr.Blocks(css=css) as demo:
         outputs = [result]
     )
 
-demo.queue().launch()
+demo.queue().launch()'''