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import gradio as gr |
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import numpy as np |
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import torch |
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from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan |
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checkpoint_base = "microsoft/speecht5_tts" |
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checkpoint_finetuned = "JackismyShephard/speecht5_tts-finetuned-nst-da" |
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processor = SpeechT5Processor.from_pretrained(checkpoint_base) |
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model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint_finetuned) |
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") |
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speaker_embeddings = { |
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"F23": "embeddings/female_23_vestjylland.npy", |
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"F24": "embeddings/female_24_storkoebenhavn.npy", |
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"F49": "embeddings/female_49_nordjylland.npy", |
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"M51": "embeddings/male_51_vest_sudsjaelland.npy", |
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"M18": "embeddings/male_18_vest_sydsjaelland.npy", |
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"M31": "embeddings/male_31_fyn.npy", |
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} |
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def predict(text, speaker): |
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if len(text.strip()) == 0: |
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return (16000, np.zeros(0)) |
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text = replace_danish_letters(text) |
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inputs = processor(text=text, return_tensors="pt") |
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input_ids = inputs["input_ids"] |
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input_ids = input_ids[..., : model.config.max_text_positions] |
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speaker_id = speaker[:3] |
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speaker_embedding_path = speaker_embeddings[speaker_id] |
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speaker_embedding = np.load(speaker_embedding_path) |
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speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0) |
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speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder) |
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speech = speech.numpy() |
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return (16000, speech) |
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def replace_danish_letters(text): |
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for src, dst in replacements: |
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text = text.replace(src, dst) |
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return text |
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replacements = [ |
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("&", "og"), |
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("\r", " "), |
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("´", ""), |
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("\\", ""), |
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("¨", " "), |
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("Å", "AA"), |
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("Æ", "AE"), |
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("É", "E"), |
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("Ö", "OE"), |
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("Ø", "OE"), |
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("á", "a"), |
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("ä", "ae"), |
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("å", "aa"), |
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("è", "e"), |
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("î", "i"), |
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("ô", "oe"), |
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("ö", "oe"), |
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("ø", "oe"), |
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("ü", "y"), |
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] |
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title = "Danish Speech Synthesis" |
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description = """ |
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synthesize long-form danish speech from text with the click of a button! Demo uses the" |
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f" checkpoint [{checkpoint_finetuned}](https://huggingface.co/{checkpoint_finetuned}) and 🤗 Transformers to synthesize speech. |
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""" |
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examples = [ |
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[ |
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"I sin oprindelige før-kristne form blev alferne sandsynligvis opfattet som en personificering af det land og den natur, der omgav menneskene, dvs. den opdyrkede jord, gården og de naturressourcer, som hørte dertil. De var guddommelige eller delvis guddommelige væsener, der besad magiske kræfter, som de brugte både til fordel og ulempe for menneskene.", |
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"F23 (Female, 23, Vestjylland)", |
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], |
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] |
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demo = gr.Interface( |
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fn=predict, |
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inputs=[ |
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gr.Textbox(label="Input Text"), |
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gr.Radio( |
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label="Speaker", |
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choices=[ |
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"F23 (Female, 23, Vestjylland)", |
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"F24 (Female, 24, Storkoebenhavn)", |
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"F49 (Female, 49 Nordjylland)", |
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"M51 (Male. 51. Vest-sydsjaelland)", |
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"M18 (Male, 18, Vest-sysjaelland)", |
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"M31 (Male, 31, Fyn)", |
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], |
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value="F23 (Female, 23, Vestjylland)", |
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), |
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], |
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outputs=[ |
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gr.Audio(label="Generated Speech", type="numpy"), |
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], |
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title=title, |
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description=description, |
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examples=examples, |
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cache_examples=True, |
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allow_flagging="never", |
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) |
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demo.launch() |
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