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| #Importing all the necessary packages | |
| import nltk | |
| import librosa | |
| import torch | |
| import gradio as gr | |
| from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC | |
| nltk.download("punkt") | |
| def asr_transcript(audio_file, language): | |
| """Generating transcripts for the audio input | |
| """ | |
| #Selecting the language and loading the model and the tokenizer | |
| if language == "English": | |
| model = "facebook/wav2vec2-large-960h-lv60-self" | |
| elif language == "Russian": | |
| model = "jonatasgrosman/wav2vec2-large-xlsr-53-russian" | |
| tokenizer = Wav2Vec2Tokenizer.from_pretrained(model) | |
| model = Wav2Vec2ForCTC.from_pretrained(model) | |
| #read the file and resample to 16KHz | |
| #stream = librosa.stream(audio_file.name, block_length=20, frame_length=16000, hop_length=16000) | |
| #read the file | |
| speech, sample_rate = librosa.load(audio_file, 16000) | |
| if len(speech.shape) > 1: | |
| speech = speech[:, 0] + speech[:, 1] | |
| input_values = tokenizer(speech, return_tensors="pt").input_values | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = tokenizer.batch_decode(predicted_ids)[0] | |
| transcript = transcription.lower() | |
| return transcript | |
| gr.Interface(asr_transcript, | |
| inputs = [gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Please record your message/Пожалуйста, введите Ваше сообщение"), | |
| gr.inputs.Radio(label="Pick a language/Выберите язык", choices=["English", "Russian"])], | |
| outputs = gr.outputs.Textbox(label="Output Text/Результат"), | |
| title="Automatic speech recognition with voice recorder in Russian and English", | |
| description = "This application displays transcribed text for given audio input/Это приложение отображает текст Вашего голосового сообщения", | |
| theme="grass").launch() |