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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # In[ ]: | |
| #Importing all the necessary packages | |
| import nltk | |
| import librosa | |
| import IPython.display | |
| import torch | |
| import gradio as gr | |
| from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC | |
| nltk.download("punkt") | |
| # In[ ]: | |
| #Loading the model and the tokenizer | |
| model_name = "facebook/wav2vec2-base-960h" | |
| #model_name = "facebook/wav2vec2-large-xlsr-53" | |
| tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)#omdel_name | |
| model = Wav2Vec2ForCTC.from_pretrained(model_name) | |
| # In[ ]: | |
| 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) | |
| #speeches = librosa.effects.split(speech) | |
| return speech | |
| # In[ ]: | |
| 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])) | |
| # In[ ]: | |
| 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 | |
| # In[ ]: | |
| def asr_transcript_long(input_file,tokenizer=tokenizer, model=model ): | |
| transcript = "" | |
| # Ensure that the sample rate is 16k | |
| sample_rate = librosa.get_samplerate(input_file) | |
| # Stream over 10 seconds chunks rather than load the full file | |
| stream = librosa.stream( | |
| input_file, | |
| block_length=20, #number of seconds to split the batch | |
| frame_length=sample_rate, #16000, | |
| hop_length=sample_rate, #16000 | |
| ) | |
| for speech in stream: | |
| if len(speech.shape) > 1: | |
| speech = speech[:, 0] + speech[:, 1] | |
| if sample_rate !=16000: | |
| speech = librosa.resample(speech, sample_rate,16000) | |
| input_values = tokenizer(speech, return_tensors="pt").input_values | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = tokenizer.decode(predicted_ids[0]) | |
| #transcript += transcription.lower() | |
| transcript += correct_casing(transcription.lower()) | |
| #transcript += " " | |
| return transcript[:3800] | |
| # In[ ]: | |
| gr.Interface(asr_transcript_long, | |
| #inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Please record your voice"), | |
| inputs = gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Upload your file here"), | |
| outputs = gr.outputs.Textbox(type="str",label="Output Text"), | |
| title="Transcript and Translate", | |
| description = "This application displays transcribed text for given audio input", | |
| examples = [["Test_File1.wav"], ["Test_File2.wav"], ["Test_File3.wav"]], theme="grass").launch() | |