| #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") | |
| #Loading the model and the tokenizer | |
| # model_name = "facebook/wav2vec2-base-960h" | |
| # tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)#omdel_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) | |
| # #speeches = librosa.effects.split(speech) | |
| # 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 | |
| # 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] | |
| from transformers import pipeline | |
| p=pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h") | |
| def asr_transcript_long(input_file): | |
| return p(input_file, chunk_length_s=10, stride_length_s=(2, 2))['text'] | |
| 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 audio file here"), | |
| outputs = gr.outputs.Textbox(type="str",label="Output Text"), | |
| title="English Automated Speech Summarization", | |
| description = "This tool transcribes your audio to the text", | |
| examples = [["sample 1.flac"], ["sample 2.flac"], ["sample 3.flac"],["TheDiverAnUncannyTale.mp3"]], | |
| theme="grass").launch() |