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import gradio as gr |
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import torch |
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from datasets import load_dataset |
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from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan |
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import soundfile as sf |
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import spaces |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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def load_models_and_data(): |
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model_name = "microsoft/speecht5_tts" |
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processor = SpeechT5Processor.from_pretrained(model_name) |
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model = SpeechT5ForTextToSpeech.from_pretrained("emirhanbilgic/speecht5_finetuned_emirhan_tr").to(device) |
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) |
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") |
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speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0).to(device) |
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return model, processor, vocoder, speaker_embeddings |
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model, processor, vocoder, speaker_embeddings = load_models_and_data() |
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@spaces.GPU(duration = 60) |
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def text_to_speech(text): |
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inputs = processor(text=text, return_tensors="pt").to(device) |
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speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) |
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sf.write("output.wav", speech.cpu().numpy(), samplerate=16000) |
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return "output.wav" |
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iface = gr.Interface( |
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fn=text_to_speech, |
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inputs=gr.Textbox(label="Enter Turkish text to convert to speech"), |
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outputs=gr.Audio(label="Generated Speech"), |
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title="Turkish SpeechT5 Text-to-Speech Demo", |
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description="Enter Turkish text and listen to the generated speech using the fine-tuned SpeechT5 model." |
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) |
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iface.launch() |
