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| # import sounddevice as sd | |
| # import soundfile as sf | |
| # import speech_recognition as sr | |
| # from gtts import gTTS | |
| # import pygame | |
| # import time | |
| # import gradio as gr | |
| # from transformers import AutoTokenizer, AutoModelForQuestionAnswering | |
| # model = AutoModelForQuestionAnswering.from_pretrained('AVISHKAARAM/avishkaarak-ekta-hindi') | |
| # tokenizer = AutoTokenizer.from_pretrained('AVISHKAARAM/avishkaarak-ekta-hindi') | |
| # class AvishkaaramEkta: | |
| # def __init__(self, model): | |
| # self.model = model | |
| # self.tokenizer = tokenizer | |
| # def text_to_speech(self, text, output_file): | |
| # # Create a gTTS object with the text and desired language | |
| # tts = gTTS(text=text, lang='en') | |
| # # Save the audio to a file | |
| # tts.save(output_file) | |
| # def play_mp3(self, file_path): | |
| # pygame.mixer.init() | |
| # pygame.mixer.music.load(file_path) | |
| # pygame.mixer.music.play() | |
| # while pygame.mixer.music.get_busy(): | |
| # continue | |
| # def ask_question(self, audio_file): | |
| # print("Recording audio...") | |
| # audio = sd.rec(int(44100 * 6), samplerate=44100, channels=1) | |
| # sd.wait() | |
| # # Save the audio to a file | |
| # sf.write(audio_file, audio, 44100) | |
| # print(f"Audio saved to {audio_file}") | |
| # r = sr.Recognizer() | |
| # with sr.AudioFile(audio_file) as source: | |
| # audio_data = r.record(source) | |
| # text = "" | |
| # try: | |
| # text = r.recognize_google(audio_data) | |
| # print("Transcription:", text) | |
| # except sr.UnknownValueError: | |
| # print("Speech recognition could not understand audio") | |
| # except sr.RequestError as e: | |
| # print("Could not request results from Google Speech Recognition service; {0}".format(e)) | |
| # return text | |
| # def answer_question(self, passage, question): | |
| # inputs = self.tokenizer(passage, question, return_tensors="pt") | |
| # outputs = self.model(**inputs) | |
| # start_logits = outputs.start_logits | |
| # end_logits = outputs.end_logits | |
| # start_index = start_logits.argmax(dim=1).item() | |
| # end_index = end_logits.argmax(dim=1).item() | |
| # tokens = self.tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) | |
| # answer = self.tokenizer.convert_tokens_to_string(tokens[start_index:end_index+1]) | |
| # return answer | |
| # def question_answer(self, passage, question): | |
| # passage_audio_file = "passage.mp3" | |
| # question_audio_file = "question.wav" | |
| # answer_audio_file = "answer.mp3" | |
| # self.text_to_speech(passage, passage_audio_file) | |
| # self.play_mp3(passage_audio_file) | |
| # question_text = self.ask_question(question_audio_file) | |
| # answer = self.answer_question(passage, question_text) | |
| # self.text_to_speech("The answer to the question is: " + answer, answer_audio_file) | |
| # self.play_mp3(answer_audio_file) | |
| # time.sleep(5) # Wait for 5 seconds before ending | |
| # return answer | |
| # # Create an instance of the AvishkaaramEkta class | |
| # avishkaaram_ekta = AvishkaaramEkta(model) | |
| # # Define the Gradio interface | |
| # iface = gr.Interface( | |
| # fn=avishkaaram_ekta.question_answer, | |
| # inputs=["text", "text"], | |
| # outputs="text", | |
| # title="Audio Question Answering", | |
| # description="Ask a question about a given passage using audio input", | |
| # examples=[ | |
| # ["In 1960, Dr. Jane Goodall arrived in Gombe, Tanzania to study chimpanzees.", "What did Dr. Jane Goodall study?"], | |
| # ["The Taj Mahal is located in Agra, India.", "Where is the Taj Mahal situated?"], | |
| # ], | |
| # interpretation="default", | |
| # ) | |
| # # Launch the Gradio interface | |
| # iface.launch() | |
| # import torch | |
| # import torchaudio | |
| # import soundfile as sf | |
| # import speech_recognition as sr | |
| # from gtts import gTTS | |
| # import pygame | |
| # import time | |
| # import gradio as gr | |
| # import os | |
| # from transformers import AutoTokenizer, AutoModelForQuestionAnswering | |
| # model = AutoModelForQuestionAnswering.from_pretrained('AVISHKAARAM/avishkaarak-ekta-hindi') | |
| # tokenizer = AutoTokenizer.from_pretrained('AVISHKAARAM/avishkaarak-ekta-hindi') | |
| # os.environ['SDL_AUDIODRIVER'] = 'dsp' | |
| # class AvishkaaramEkta: | |
| # def __init__(self, model): | |
| # self.model = model | |
| # self.tokenizer = tokenizer | |
| # def text_to_speech(self, text, output_file): | |
| # # Create a gTTS object with the text and desired language | |
| # tts = gTTS(text=text, lang='en') | |
| # # Save the audio to a file | |
| # tts.save(output_file) | |
| # def play_mp3(self, file_path): | |
| # pygame.mixer.init() | |
| # pygame.mixer.music.load(file_path) | |
| # pygame.mixer.music.play() | |
| # while pygame.mixer.music.get_busy(): | |
| # continue | |
| # def ask_question(self, audio_file): | |
| # print("Recording audio...") | |
| # waveform, sample_rate = torchaudio.rec(6, sr=44100, channels=1) | |
| # # Save the audio to a file | |
| # sf.write(audio_file, waveform.squeeze().numpy(), sample_rate) | |
| # print(f"Audio saved to {audio_file}") | |
| # r = sr.Recognizer() | |
| # with sr.AudioFile(audio_file) as source: | |
| # audio_data = r.record(source) | |
| # text = "" | |
| # try: | |
| # text = r.recognize_google(audio_data) | |
| # print("Transcription:", text) | |
| # except sr.UnknownValueError: | |
| # print("Speech recognition could not understand audio") | |
| # except sr.RequestError as e: | |
| # print("Could not request results from Google Speech Recognition service; {0}".format(e)) | |
| # return text | |
| # def answer_question(self, passage, question): | |
| # inputs = self.tokenizer(passage, question, return_tensors="pt") | |
| # outputs = self.model(**inputs) | |
| # start_logits = outputs.start_logits | |
| # end_logits = outputs.end_logits | |
| # start_index = start_logits.argmax(dim=1).item() | |
| # end_index = end_logits.argmax(dim=1).item() | |
| # tokens = self.tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) | |
| # answer = self.tokenizer.convert_tokens_to_string(tokens[start_index:end_index+1]) | |
| # return answer | |
| # def question_answer(self, passage, question): | |
| # passage_audio_file = "passage.mp3" | |
| # question_audio_file = "question.wav" | |
| # answer_audio_file = "answer.mp3" | |
| # self.text_to_speech(passage, passage_audio_file) | |
| # self.play_mp3(passage_audio_file) | |
| # question_text = self.ask_question(question_audio_file) | |
| # answer = self.answer_question(passage, question_text) | |
| # self.text_to_speech("The answer to the question is: " + answer, answer_audio_file) | |
| # self.play_mp3(answer_audio_file) | |
| # time.sleep(5) # Wait for 5 seconds before ending | |
| # return answer | |
| # # Create an instance of the AvishkaaramEkta class | |
| # avishkaaram_ekta = AvishkaaramEkta(model) | |
| # # Define the Gradio interface | |
| # iface = gr.Interface( | |
| # fn=avishkaaram_ekta.question_answer, | |
| # inputs=["text", "text"], | |
| # outputs="text", | |
| # title="Audio Question Answering", | |
| # description="Ask a question about a given passage using audio input", | |
| # examples=[ | |
| # ["In 1960, Dr. Jane Goodall arrived in Gombe, Tanzania to study chimpanzees.", "What did Dr. Jane Goodall study?"], | |
| # ["The Taj Mahal is located in Agra, India.", "Where is the Taj Mahal situated?"], | |
| # ], | |
| # interpretation="default", | |
| # ) | |
| # # Launch the Gradio interface | |
| # iface.launch() | |
| import torch | |
| import torchaudio | |
| import soundfile as sf | |
| import speech_recognition as sr | |
| from gtts import gTTS | |
| import pygame | |
| import time | |
| import os | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForQuestionAnswering | |
| os.environ["SDL_AUDIODRIVER"] = "pulseaudio" | |
| model = AutoModelForQuestionAnswering.from_pretrained('AVISHKAARAM/avishkaarak-ekta-hindi') | |
| tokenizer = AutoTokenizer.from_pretrained('AVISHKAARAM/avishkaarak-ekta-hindi') | |
| class AvishkaaramEkta: | |
| def __init__(self, model): | |
| self.model = model | |
| self.tokenizer = tokenizer | |
| def text_to_speech(self, text, output_file): | |
| # Create a gTTS object with the text and desired language | |
| tts = gTTS(text=text, lang='en') | |
| # Save the audio to a file | |
| tts.save(output_file) | |
| def play_mp3(self, file_path): | |
| pygame.mixer.init() | |
| pygame.mixer.music.load(file_path) | |
| pygame.mixer.music.play() | |
| while pygame.mixer.music.get_busy(): | |
| continue | |
| def ask_question(self, audio_file): | |
| print("Recording audio...") | |
| waveform, sample_rate = torchaudio.rec(6, sr=44100, channels=1) | |
| # Save the audio to a file | |
| sf.write(audio_file, waveform.squeeze().numpy(), sample_rate) | |
| print(f"Audio saved to {audio_file}") | |
| r = sr.Recognizer() | |
| with sr.AudioFile(audio_file) as source: | |
| audio_data = r.record(source) | |
| text = "" | |
| try: | |
| text = r.recognize_google(audio_data) | |
| print("Transcription:", text) | |
| except sr.UnknownValueError: | |
| print("Speech recognition could not understand audio") | |
| except sr.RequestError as e: | |
| print("Could not request results from Google Speech Recognition service; {0}".format(e)) | |
| return text | |
| def answer_question(self, passage, question): | |
| inputs = self.tokenizer(passage, question, return_tensors="pt") | |
| outputs = self.model(**inputs) | |
| start_logits = outputs.start_logits | |
| end_logits = outputs.end_logits | |
| start_index = start_logits.argmax(dim=1).item() | |
| end_index = end_logits.argmax(dim=1).item() | |
| tokens = self.tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) | |
| answer = self.tokenizer.convert_tokens_to_string(tokens[start_index:end_index+1]) | |
| return answer | |
| def question_answer(self, passage, question): | |
| passage_audio_file = "passage.mp3" | |
| question_audio_file = "question.wav" | |
| answer_audio_file = "answer.mp3" | |
| self.text_to_speech(passage, passage_audio_file) | |
| self.play_mp3(passage_audio_file) | |
| question_text = self.ask_question(question_audio_file) | |
| answer = self.answer_question(passage, question_text) | |
| self.text_to_speech("The answer to the question is: " + answer, answer_audio_file) | |
| self.play_mp3(answer_audio_file) | |
| time.sleep(5) # Wait for 5 seconds before ending | |
| return answer | |
| # Create an instance of the AvishkaaramEkta class | |
| avishkaaram_ekta = AvishkaaramEkta(model) | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=avishkaaram_ekta.question_answer, | |
| inputs=["text", "text"], | |
| outputs="text", | |
| title="Audio Question Answering", | |
| description="Ask a question about a given passage using audio input", | |
| examples=[ | |
| ["In 1960, Dr. Jane Goodall arrived in Gombe, Tanzania, to study the behavior of chimpanzees in the wild."], | |
| ["What was the purpose of Dr. Jane Goodall's visit to Gombe?"] | |
| ] | |
| ) | |
| # Launch the interface | |
| iface.launch() | |