Create app.py

app.py

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+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"
+
+# load speech translation checkpoint
+asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device=device)
+
+# load text-to-speech checkpoint and speaker embeddings
+model_id = "Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german"  # update with your model id
+# pipe = pipeline("automatic-speech-recognition", model=model_id)
+model = SpeechT5ForTextToSpeech.from_pretrained(model_id)
+processor = SpeechT5Processor.from_pretrained(model_id)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0)
+
+replacements = [    
+    ("Ä", "E"),
+    ("Æ", "E"),
+    ("Ç", "C"),
+    ("É", "E"),
+    ("Í", "I"),
+    ("Ó", "O"),
+    ("Ö", "E"),
+    ("Ü", "Y"),
+    ("ß", "S"),
+    ("à", "a"),
+    ("á", "a"),
+    ("ã", "a"),
+    ("ä", "e"),
+    ("å", "a"),
+    ("ë", "e"),
+    ("í", "i"),
+    ("ï", "i"),
+    ("ð", "o"),
+    ("ñ", "n"),
+    ("ò", "o"),
+    ("ó", "o"),
+    ("ô", "o"),
+    ("ö", "u"),
+    ("ú", "u"),
+    ("ü", "y"),
+    ("ý", "y"),
+    ("Ā", "A"),
+    ("ā", "a"),
+    ("ă", "a"),
+    ("ą", "a"),
+    ("ć", "c"),
+    ("Č", "C"),
+    ("č", "c"),
+    ("ď", "d"),
+    ("Đ", "D"),
+    ("ę", "e"),
+    ("ě", "e"),
+    ("ğ", "g"),
+    ("İ", "I"),
+    ("О", "O"),
+    ("Ł", "L"),
+    ("ń", "n"),
+    ("ň", "n"),
+    ("Ō", "O"),
+    ("ō", "o"),
+    ("ő", "o"),
+    ("ř", "r"),
+    ("Ś", "S"),
+    ("ś", "s"),
+    ("Ş", "S"),
+    ("ş", "s"),
+    ("Š", "S"),
+    ("š", "s"),
+    ("ū", "u"),
+    ("ź", "z"),
+    ("Ż", "Z"),
+    ("Ž", "Z"),
+    ("ǐ", "i"),
+    ("ǐ", "i"),
+    ("ș", "s"),
+    ("ț", "t"),
+]
+
+
+def cleanup_text(text):
+    for src, dst in replacements:
+        text = text.replace(src, dst)
+    return text
+
+
+def transcribe_to_german(audio):
+    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "german"})
+    return outputs["text"]
+
+
+def synthesise_from_german(text):
+    text = cleanup_text(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()
+
+
+def speech_to_speech_translation(audio):
+    translated_text = transcribe_to_german(audio)
+    synthesised_speech = synthesise_from_german(translated_text)
+    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
+    return ((16000, synthesised_speech), translated_text)
+
+
+title = "Cascaded STST"
+description = """
+Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in German. Demo uses OpenAI's [Whisper Large v2](https://huggingface.co/openai/whisper-large-v2) model for speech translation, and [Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german](https://huggingface.co/Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german) checkpoint for text-to-speech, which is based on Microsoft's
+[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech, fine-tuned in German Audio dataset:
+![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"),
+    outputs=[gr.Audio(label="Generated Speech", type="numpy"), gr.outputs.Textbox()],
+    title=title,
+    description=description,
+)
+
+file_translate = gr.Interface(
+    fn=speech_to_speech_translation,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs=[gr.Audio(label="Generated Speech", type="numpy"), gr.outputs.Textbox()],
+    examples=[["./example.wav"]],
+    title=title,
+    description=description,
+)
+
+with demo:
+    gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
+
+demo.launch()