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| import soundfile as sf | |
| import torch | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer | |
| import gradio as gr | |
| import sox | |
| import numpy as np | |
| import yaml | |
| import tensorflow as tf | |
| from tensorflow_tts.inference import TFAutoModel | |
| from tensorflow_tts.inference import AutoProcessor | |
| # initialize fastspeech2 model. | |
| fastspeech2 = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en") | |
| # initialize mb_melgan model | |
| mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en") | |
| # inference | |
| processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech2-ljspeech-en") | |
| def tts(text): | |
| input_ids = processor.text_to_sequence(text) | |
| # fastspeech inference | |
| mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference( | |
| input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0), | |
| speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32), | |
| speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32), | |
| f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32), | |
| energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32), | |
| ) | |
| # melgan inference | |
| audio_before = mb_melgan.inference(mel_before)[0, :, 0] | |
| audio_after = mb_melgan.inference(mel_after)[0, :, 0] | |
| # save to file | |
| sf.write('./audio_before.wav', audio_before, 22050, "PCM_16") | |
| sf.write('./audio_after.wav', audio_after, 22050, "PCM_16") | |
| return './audio_after.wav' | |
| def convert(inputfile, outfile): | |
| sox_tfm = sox.Transformer() | |
| sox_tfm.set_output_format( | |
| file_type="wav", channels=1, encoding="signed-integer", rate=16000, bits=16 | |
| ) | |
| sox_tfm.build(inputfile, outfile) | |
| model_translate = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M") | |
| tokenizer_translate = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M") | |
| inlang='hi' | |
| outlang='en' | |
| tokenizer_translate.src_lang = inlang | |
| def translate(text): | |
| encoded_hi = tokenizer_translate(text, return_tensors="pt") | |
| generated_tokens = model_translate.generate(**encoded_hi, forced_bos_token_id=tokenizer_translate.get_lang_id(outlang)) | |
| return tokenizer_translate.batch_decode(generated_tokens, skip_special_tokens=True)[0] | |
| processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") | |
| def parse_transcription(wav_file): | |
| filename = wav_file.name.split('.')[0] | |
| convert(wav_file.name, filename + "16k.wav") | |
| speech, _ = sf.read(filename + "16k.wav") | |
| input_values = processor(speech, sampling_rate=16_000, return_tensors="pt").input_values | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) | |
| translation = translate(transcription) | |
| return transcription, translation, tts(translation) | |
| output1 = gr.outputs.Textbox(label="Hindi Output from ASR") | |
| output2 = gr.outputs.Textbox(label="English Translated Output") | |
| input_ = gr.inputs.Audio(source="microphone", type="file") | |
| output_audio = gr.outputs.Audio(type="file", label="Output Audio") | |
| gr.Interface(parse_transcription, inputs = input_, outputs=[output1, output2, output_audio], analytics_enabled=False, | |
| show_tips=False, | |
| theme='huggingface', | |
| layout='vertical', | |
| title="Vakyansh: Speech To text for Indic Languages", | |
| description="This is a live demo for Speech to Speech Translation. Speak in Hindi and get output in English", enable_queue=True).launch( inline=False) | |