File size: 3,840 Bytes
87d5502 1c86fc4 87d5502 1c86fc4 87d5502 1c86fc4 87d5502 1c86fc4 87d5502 1c86fc4 87d5502 1c86fc4 87d5502 1c86fc4 87d5502 1c86fc4 87d5502 1263980 d876c92 1c86fc4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
#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() |