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import torch |
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from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor, SpeechT5HifiGan |
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import soundfile as sf |
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import gradio as gr |
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import scipy.io.wavfile as wav |
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import numpy as np |
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import wave |
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from datasets import load_dataset, Audio, config |
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from IPython.display import Audio |
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checkpoint = "arham061/speecht5_finetuned_voxpopuli_nl" |
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processor = SpeechT5Processor.from_pretrained(checkpoint) |
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model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint) |
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tokenizer = processor.tokenizer |
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") |
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buck2uni = { |
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u"\u0627":"A", |
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u"\u0627":"A", |
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u"\u0675":"A", |
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u"\u0673":"A", |
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u"\u0630":"A", |
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u"\u0622":"AA", |
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u"\u0628":"B", |
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u"\u067E":"P", |
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u"\u062A":"T", |
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u"\u0637":"T", |
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u"\u0679":"T", |
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u"\u062C":"J", |
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u"\u0633":"S", |
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u"\u062B":"S", |
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u"\u0635":"S", |
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u"\u0686":"CH", |
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u"\u062D":"H", |
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u"\u0647":"H", |
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u"\u0629":"H", |
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u"\u06DF":"H", |
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u"\u062E":"KH", |
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u"\u062F":"D", |
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u"\u0688":"D", |
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u"\u0630":"Z", |
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u"\u0632":"Z", |
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u"\u0636":"Z", |
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u"\u0638":"Z", |
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u"\u068E":"Z", |
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u"\u0631":"R", |
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u"\u0691":"R", |
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u"\u0634":"SH", |
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u"\u063A":"GH", |
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u"\u0641":"F", |
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u"\u06A9":"K", |
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u"\u0642":"K", |
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u"\u06AF":"G", |
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u"\u0644":"L", |
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u"\u0645":"M", |
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u"\u0646":"N", |
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u"\u06BA":"N", |
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u"\u0648":"O", |
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u"\u0649":"Y", |
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u"\u0626":"Y", |
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u"\u06CC":"Y", |
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u"\u06D2":"E", |
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u"\u06C1":"H", |
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u"\u064A":"E" , |
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u"\u06C2":"AH" , |
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u"\u06BE":"H" , |
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u"\u0639":"A" , |
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u"\u0643":"K" , |
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u"\u0621":"A", |
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u"\u0624":"O", |
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u"\u060C":"" |
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} |
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def transString(string, reverse=0): |
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"""Given a Unicode string, transliterate into Buckwalter. To go from |
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Buckwalter back to Unicode, set reverse=1""" |
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for k, v in buck2uni.items(): |
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if not reverse: |
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string = string.replace(k, v) |
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else: |
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string = string.replace(v, k) |
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return string |
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def generate_audio(text): |
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roman_urdu = transString(text) |
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inputs = processor(text=roman_urdu, return_tensors="pt", type = "numpy") |
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speaker_embeddings = torch.tensor(np.load("speaker_embeddings.npy")) |
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speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) |
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return speech |
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def text_to_speech(text): |
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audio_output = generate_audio(text) |
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output_path = "output.wav" |
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sf.write(output_path, audio_output.numpy(), 16000, "PCM_16") |
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return output_path |
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examples = [ |
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['اگر رشتے داری ہے تو پیسے کی'], |
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['مجھے کھانے سے لطف نہیں آیا۔'] |
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] |
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interface = gr.Interface(fn=text_to_speech, inputs="text", outputs="audio", verbose = True, title="Urdu TTS", |
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description = "A simple Urdu Text to Speech Application. It is not by any means perfect and will not work for all text. You can sometimes expect it to generate random noise on an input of your choice. Right now it works successfully on very basic urdu text, such the ones in the example.", examples = examples) |
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interface.launch() |
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