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| import base64 | |
| import io | |
| import numpy as np | |
| import torch | |
| from fastapi import HTTPException | |
| from indicnlp.tokenize import sentence_tokenize | |
| from mosestokenizer import MosesSentenceSplitter | |
| from scipy.io.wavfile import write | |
| from tts_infer.num_to_word_on_sent import normalize_nums | |
| from src import log_setup | |
| from src.infer.model_inference import ModelService | |
| from src.model.language import Language | |
| from src.model.tts_request import TTSRequest | |
| from src.model.tts_response import TTSResponse, AudioFile, AudioConfig | |
| LOGGER = log_setup.get_logger(__name__) | |
| model_service = ModelService() | |
| _INDIC = ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"] | |
| _PURAM_VIRAM_LANGUAGES = ["hi", "or", "bn", "as"] | |
| _TRANSLITERATION_NOT_AVAILABLE_IN = ["en","or"] | |
| def infer_tts_request(request: TTSRequest): | |
| config = request.config | |
| lang = config.language.sourceLanguage | |
| gender = config.gender | |
| output_list = [] | |
| audio_config = AudioConfig(language=Language(sourceLanguage=lang)) | |
| try: | |
| for sentence in request.input: | |
| LOGGER.debug(f'infer for gender {gender} and lang {lang} text {sentence.source}') | |
| speech_response = infer_tts(language=lang, gender=gender, text_to_infer=sentence.source) | |
| LOGGER.debug(f'infer done for text {sentence.source}') | |
| output_list.append(speech_response) | |
| return TTSResponse(audio=output_list, config=audio_config) | |
| except Exception as e: | |
| LOGGER.exception('Failed to infer %s', e) | |
| raise e | |
| def infer_tts(language: str, gender: str, text_to_infer: str): | |
| choice = language + "_" + gender | |
| LOGGER.debug(f'choice for model {choice}') | |
| if choice in model_service.available_choice.keys(): | |
| t2s = model_service.available_choice[choice] | |
| else: | |
| raise NotImplementedError('Requested model not found') | |
| if text_to_infer: | |
| text_to_infer = normalize_text(text_to_infer, language) | |
| # if len(text_to_infer) > settings.tts_max_text_limit: | |
| LOGGER.debug("Running in paragraph mode...") | |
| audio, sr = run_tts_paragraph(text_to_infer, language, t2s) | |
| # else: | |
| # LOGGER.debug("Running in text mode...") | |
| # audio, sr = run_tts(text_to_infer, language, t2s) | |
| torch.cuda.empty_cache() # TODO: find better approach for this | |
| LOGGER.debug('Audio generates successfully') | |
| bytes_wav = bytes() | |
| byte_io = io.BytesIO(bytes_wav) | |
| write(byte_io, sr, audio) | |
| encoded_bytes = base64.b64encode(byte_io.read()) | |
| encoded_string = encoded_bytes.decode() | |
| LOGGER.debug(f'Encoded Audio string {encoded_string}') | |
| return AudioFile(audioContent=encoded_string) | |
| else: | |
| raise HTTPException(status_code=400, detail={"error": "No text"}) | |
| def split_sentences(paragraph, language): | |
| if language == "en": | |
| with MosesSentenceSplitter(language) as splitter: | |
| return splitter([paragraph]) | |
| elif language in _INDIC: | |
| return sentence_tokenize.sentence_split(paragraph, lang=language) | |
| def normalize_text(text, lang): | |
| if lang in _PURAM_VIRAM_LANGUAGES: | |
| text = text.replace('|', '।') | |
| text = text.replace('.', '।') | |
| return text | |
| def pre_process_text(text, lang): | |
| if lang == 'hi': | |
| text = text.replace('।', '.') # only for hindi models | |
| if lang == 'en' and text[-1] != '.': | |
| text = text + '. ' | |
| return text | |
| def run_tts_paragraph(text, lang, t2s): | |
| audio_list = [] | |
| split_sentences_list = split_sentences(text, language=lang) | |
| for sent in split_sentences_list: | |
| audio, sr = run_tts(pre_process_text(sent, lang), lang, t2s) | |
| audio_list.append(audio) | |
| concatenated_audio = np.concatenate([i for i in audio_list]) | |
| # write(filename='temp_long.wav', rate=sr, data=concatenated_audio) | |
| return concatenated_audio, sr | |
| def run_tts(text, lang, t2s): | |
| text_num_to_word = normalize_nums(text, lang) # converting numbers to words in lang | |
| if lang not in _TRANSLITERATION_NOT_AVAILABLE_IN: | |
| text_num_to_word_and_transliterated = model_service.transliterate_obj.translit_sentence(text_num_to_word, | |
| lang) # transliterating english words to lang | |
| else: | |
| text_num_to_word_and_transliterated = text_num_to_word | |
| mel = t2s[0].generate_mel(' ' + text_num_to_word_and_transliterated) | |
| audio, sr = t2s[1].generate_wav(mel) | |
| return audio, sr | |