import gradio as gr import librosa import numpy as np import torch import string import httpx from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan checkpoint = "microsoft/speecht5_tts" processor = SpeechT5Processor.from_pretrained(checkpoint) model = SpeechT5ForTextToSpeech.from_pretrained("Edmon02/speecht5_finetuned_voxpopuli_hy") vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") speaker_embeddings = { "BDL": "cmu_us_bdl_arctic-wav-arctic_a0009.npy", } import pandas as pd import inflect import re from googletrans import Translator translator = Translator() def convert_number_to_words(number: float) -> str: p = inflect.engine() words = p.number_to_words(number) # Translate using httpx async def translate_text(text, source_lang, target_lang): async with httpx.AsyncClient() as client: response = await client.get( f'https://api.mymemory.translated.net/get?q={text}&langpair={source_lang}|{target_lang}' ) translation = response.json() return translation['responseData']['translatedText'] # You can change 'en' to the appropriate source language code source_lang = 'en' # You can change 'hy' to the appropriate target language code target_lang = 'hy' # Perform translation asynchronously translated_words = httpx.run(translate_text, words, source_lang, target_lang) return translated_words def process_text(text: str) -> str: # Convert numbers to words words = [] text = str(text) if str(text) else '' for word in text.split(): # Check if the word is a number if re.search(r'\d', word): words.append(convert_number_to_words(int(''.join(filter(str.isdigit, word))))) else: words.append(word) # Join the words back into a sentence processed_text = ' '.join(words) return processed_text # Read CSV file into a pandas DataFrame df = pd.read_csv('AudioSet.csv') # Apply the processing function to the 'normalized_text' column df['normalized_text'] = df['normalized_text'].apply(process_text) # Save the updated DataFrame back to the CSV file df.to_csv('AudioSet.csv', index=False) replacements = [ ("՚", "?"), ('՛', ""), ('՝', ""), ("«", "\""), ("»", "\""), ("՞", "?"), ("ա", "a"), ("բ", "b"), ("գ", "g"), ("դ", "d"), ("զ", "z"), ("է", "e"), ("ը", "e'"), ("թ", "t'"), ("ժ", "jh"), ("ի", "i"), ("լ", "l"), ("խ", "kh"), ("ծ", "ts"), ("կ", "k"), ("հ", "h"), ("ձ", "dz"), ("ղ", "gh"), ("ճ", "ch"), ("մ", "m"), ("յ", "y"), ("ն", "n"), ("շ", "sh"), ("չ", "ch'"), ("պ", "p"), ("ջ", "j"), ("ռ", "r"), ("ս", "s"), ("վ", "v"), ("տ", "t"), ("ր", "r"), ("ց", "ts'"), ("ւ", ""), ("փ", "p'"), ("ք", "k'"), ("և", "yev"), ("օ", "o"), ("ֆ", "f"), ('։', "."), ('–', "-"), ('†', "e'"), ] def cleanup_text(text): translator = str.maketrans("", "", string.punctuation) text = text.translate(translator).lower() text = text.lower() normalized_text = text normalized_text = normalized_text.replace("ու", "u") normalized_text = normalized_text.replace("եւ", "u") normalized_text = normalized_text.replace("եվ", "u") # Handle 'ո' at the beginning of a word normalized_text = normalized_text.replace(" ո", " vo") # Handle 'ո' in the middle of a word normalized_text = normalized_text.replace("ո", "o") # Handle 'ե' at the beginning of a word normalized_text = normalized_text.replace(" ե", " ye") # Handle 'ե' in the middle of a word normalized_text = normalized_text.replace("ե", "e") # Apply other replacements for src, dst in replacements: normalized_text = normalized_text.replace(src, dst) inputs = normalized_text return inputs def predict(text, speaker): if len(text.strip()) == 0: return (16000, np.zeros(0).astype(np.int16)) text = process_text(text) text = cleanup_text({'normalized_text': text})['normalized_text'] inputs = processor(text=text, return_tensors="pt") # limit input length input_ids = inputs["input_ids"] input_ids = input_ids[..., :model.config.max_text_positions] speaker_embedding = np.load(speaker_embeddings[speaker[:3]]) speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0) speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder) speech = (speech.numpy() * 32767).astype(np.int16) return (16000, speech) title = "SpeechT5: Speech Synthesis" description = """ The SpeechT5 model is pre-trained on text as well as speech inputs, with targets that are also a mix of text and speech. By pre-training on text and speech at the same time, it learns unified representations for both, resulting in improved modeling capabilities. SpeechT5 can be fine-tuned for different speech tasks. This space demonstrates the text-to-speech (TTS) checkpoint for the English language. See also the speech recognition (ASR) demo and the voice conversion demo. Refer to this Colab notebook to learn how to fine-tune the SpeechT5 TTS model on your own dataset or language. How to use: Enter some English text and choose a speaker. The output is a mel spectrogram, which is converted to a mono 16 kHz waveform by the HiFi-GAN vocoder. Because the model always applies random dropout, each attempt will give slightly different results. The Surprise Me! option creates a completely randomized speaker. """ article = """

References: SpeechT5 paper | original GitHub | original weights

@article{Ao2021SpeechT5,
  title   = {SpeechT5: Unified-Modal Encoder-Decoder Pre-training for Spoken Language Processing},
  author  = {Junyi Ao and Rui Wang and Long Zhou and Chengyi Wang and Shuo Ren and Yu Wu and Shujie Liu and Tom Ko and Qing Li and Yu Zhang and Zhihua Wei and Yao Qian and Jinyu Li and Furu Wei},
  eprint={2110.07205},
  archivePrefix={arXiv},
  primaryClass={eess.AS},
  year={2021}
}

Speaker embeddings were generated from CMU ARCTIC using this script.

""" examples = [ ["It is not in the stars to hold our destiny but in ourselves.", "BDL (male)"], ["The octopus and Oliver went to the opera in October.", "CLB (female)"], ["She sells seashells by the seashore. I saw a kitten eating chicken in the kitchen.", "RMS (male)"], ["Brisk brave brigadiers brandished broad bright blades, blunderbusses, and bludgeons—balancing them badly.", "SLT (female)"], ["A synonym for cinnamon is a cinnamon synonym.", "BDL (male)"], ["How much wood would a woodchuck chuck if a woodchuck could chuck wood? He would chuck, he would, as much as he could, and chuck as much wood as a woodchuck would if a woodchuck could chuck wood.", "CLB (female)"], ] gr.Interface( fn=predict, inputs=[ gr.Text(label="Input Text"), gr.Radio(label="Speaker", choices=[ "BDL (male)" ], value="BDL (male)"), ], outputs=[ gr.Audio(label="Generated Speech", type="numpy"), ], title=title, description=description, article=article, examples=examples, ).launch()