Upload app.py

app.py

@@ -0,0 +1,57 @@
+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": "hi"}
+    )
+
+model = VitsModel.from_pretrained("facebook/mms-tts-deu")
+tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-deu")
+
+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(source="microphone", type="filepath"),
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
+)
+
+file_translate = gr.Interface(
+    fn=speech_to_speech_translation,
+    inputs=gr.Audio(source="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)