Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import torch
+from transformers import AutoProcessor, pipeline, BarkModel
+
+ASR_MODEL_NAME = "VinayHajare/whisper-small-finetuned-common-voice-mr"
+TTS_MODEL_NAME = "suno/bark-small"
+BATCH_SIZE = 8
+voices = {
+"male" : "v2/en_speaker_6",
+"female" : "v2/en_speaker_9"
+}
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# load speech translation checkpoint
+asr_pipe = pipeline("automatic-speech-recognition", model=ASR_MODEL_NAME, chunk_length_s=30,device=device)
+
+# load text-to-speech checkpoint
+processor = AutoProcessor.from_pretrained("suno/bark-small")
+model = BarkModel.from_pretrained("suno/bark-small").to(device)
+sampling_rate = model.generation_config.sample_rate
+
+def translate(audio):
+    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
+    return outputs["text"]
+    
+def synthesise(text, voice_preset):
+    inputs = processor(text=text, return_tensors="pt",voice_preset=voice_preset)
+    speech = model.generate(**inputs.to(device))
+    return speech[0]
+    
+def speech_to_speech_translation(audio, voice):
+    voice_preset = None
+    translated_text = translate(audio)
+    print(translated_text)
+    if voice == "Female":
+     voice_preset = voices["female"]
+    else:
+     voice_preset = voices["male"]
+    synthesised_speech = synthesise(translated_text, voice_preset)
+    synthesised_speech = (synthesised_speech.cpu().numpy() * 32767).astype(np.int16)
+    return sampling_rate, synthesised_speech
+    
+title = "Cascaded STST for Marathi to English"
+description = """
+Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any Marathi to target speech in English. Demo uses OpenAI's [Whisper Small](https://huggingface.co/VinayHajare/whisper-small-finetuned-common-voice-mr) model for speech translation, and Suno's
+[Bark-large](https://huggingface.co/suno/bark-small) 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")
+"""
+demo = gr.Blocks()
+
+mic_translate = gr.Interface(
+    fn=speech_to_speech_translation,
+    inputs=[gr.Audio(source="microphone", type="filepath"),
+    gr.inputs.Radio(["Male", "Female"], label="Voice", default="Male")],
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
+    title=title,
+    description=description,
+    allow_flagging="never"
+)
+
+file_translate = gr.Interface(
+    fn=speech_to_speech_translation,
+    inputs=[gr.Audio(source="upload", type="filepath"),
+    gr.inputs.Radio(["Male", "Female"], label="Voice", default="Male")],
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
+    title=title,
+    description=description,
+    allow_flagging="never"
+)
+
+with demo:
+    gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
+
+demo.launch(enable_queue=True)