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'''
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import speech_recognition as sr
import io
from pydub import AudioSegment
import librosa
import whisper
from scipy.io import wavfile
from test import record_voice
model = Wav2Vec2ForCTC.from_pretrained(r'yongjian/wav2vec2-large-a') # Note: PyTorch Model
tokenizer = Wav2Vec2Processor.from_pretrained(r'yongjian/wav2vec2-large-a')
r = sr.Recognizer()
from transformers import pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
with sr.Microphone(sample_rate=16000) as source:
print("You can start speaking now")
record_voice()
x,_ = librosa.load("output.wav")
model_inputs = tokenizer(x, sampling_rate=16000, return_tensors="pt", padding=True)
logits = model(model_inputs.input_values, attention_mask=model_inputs.attention_mask).logits.cuda() # use .cuda() for GPU acceleration
pred_ids = torch.argmax(logits, dim=-1).cpu()
pred_text = tokenizer.batch_decode(pred_ids)
print(x[:10],x.shape)
print('Transcription:', pred_text)
model = whisper.load_model("base")
result = model.transcribe("output.wav")
print(result["text"])
summary_input = result["text"]
summary_output = (summarizer(summary_input, max_length=30, min_length=20, do_sample=False))
print(summary_output)
with open("raw_text.txt",'w',encoding = 'utf-8') as f:
f.write(summary_input)
f.close()
with open("summary_text.txt",'w',encoding = 'utf-8') as f:
f.write(summary_output[0]["summary_text"])
f.close()
''' |