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| #References: 1. https://www.kdnuggets.com/2021/03/speech-text-wav2vec.html | |
| #2. https://www.youtube.com/watch?v=4CoVcsxZphE | |
| #3. https://www.analyticsvidhya.com/blog/2021/02/hugging-face-introduces-the-first-automatic-speech-recognition-model-wav2vec2/ | |
| #Importing all the necessary packages | |
| import nltk | |
| import librosa | |
| import torch | |
| import gradio as gr | |
| from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC | |
| nltk.download("punkt") | |
| #Loading the model and the tokenizer | |
| model_name = "wasertech/wav2vec2-cv-fr-9" | |
| tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name) | |
| model = Wav2Vec2ForCTC.from_pretrained(model_name) | |
| def load_data(input_file): | |
| """ Function for resampling to ensure that the speech input is sampled at 16KHz. | |
| """ | |
| #read the file | |
| speech, sample_rate = librosa.load(input_file) | |
| #make it 1-D | |
| if len(speech.shape) > 1: | |
| speech = speech[:,0] + speech[:,1] | |
| #Resampling at 16KHz since wav2vec2-base-960h is pretrained and fine-tuned on speech audio sampled at 16 KHz. | |
| if sample_rate !=16000: | |
| speech = librosa.resample(speech, sample_rate,16000) | |
| return speech | |
| def correct_casing(input_sentence): | |
| """ This function is for correcting the casing of the generated transcribed text | |
| """ | |
| sentences = nltk.sent_tokenize(input_sentence) | |
| return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences])) | |
| def asr_transcript(input_file): | |
| """This function generates transcripts for the provided audio input | |
| """ | |
| speech = load_data(input_file) | |
| #Tokenize | |
| input_values = tokenizer(speech, return_tensors="pt").input_values | |
| #Take logits | |
| logits = model(input_values).logits | |
| #Take argmax | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| #Get the words from predicted word ids | |
| transcription = tokenizer.decode(predicted_ids[0]) | |
| #Output is all upper case | |
| transcription = correct_casing(transcription.lower()) | |
| return transcription | |
| gr.Interface(asr_transcript, | |
| inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Démarrer l'enregistrement"), | |
| outputs = gr.outputs.Textbox(label="Transcription"), | |
| title="🎙️ Parlez, on vous écoute !", | |
| description = "Enregistrez un audio ou utilisez les examples pour interagir avec notre dernier modèle.", | |
| examples = [["wav/1.wav"], ["wav/2.wav"], ["wav/3.wav"], ["wav/4.wav"], ["wav/5.wav"]], theme="grass").launch() | |