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| import warnings | |
| warnings.filterwarnings("ignore") | |
| import gradio as gr | |
| from transformers import pipeline | |
| import os | |
| import re | |
| #import torchaudio | |
| # Initialize the speech recognition pipeline and transliterator | |
| pipe = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-odia_v1") | |
| p1 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-odia_v1") | |
| p2 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-hindi_v1") | |
| os.system('git clone https://github.com/irshadbhat/indic-trans.git') | |
| os.system('pip install ./indic-trans/.') | |
| #HF_TOKEN = os.getenv('HF_TOKEN') | |
| #hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "asr_demo") | |
| from indictrans import Transliterator | |
| trn = Transliterator(source='ori', target='eng', build_lookup=True) | |
| def transcribe_odiya(speech): | |
| text = p1(speech)["text"] | |
| if text is None: | |
| return "Error: ASR returned None" | |
| return text | |
| def transcribe_hindi(speech): | |
| text = p2(speech)["text"] | |
| if text is None: | |
| return "Error: ASR returned None" | |
| return text | |
| def transcribe_odiya_eng(speech): | |
| trn = Transliterator(source='ori', target='eng', build_lookup=True) | |
| text = p1(speech)["text"] | |
| if text is None: | |
| return "Error: ASR returned None" | |
| sentence = trn.transform(text) | |
| if sentence is None: | |
| return "Error: Transliteration returned None" | |
| replaced_words = replace_words(sentence) | |
| processed_sentence = process_doubles(replaced_words) | |
| return process_transcription(processed_sentence) | |
| def transcribe_hin_eng(speech): | |
| trn = Transliterator(source='hin', target='eng', build_lookup=True) | |
| text = p2(speech)["text"] | |
| if text is None: | |
| return "Error: ASR returned None" | |
| sentence = trn.transform(text) | |
| if sentence is None: | |
| return "Error: Transliteration returned None" | |
| replaced_words = replace_words(sentence) | |
| processed_sentence = process_doubles(replaced_words) | |
| return process_transcription(processed_sentence) | |
| def process_transcription(input_sentence): | |
| word_to_code_map = {} | |
| code_to_word_map = {} | |
| transcript_1 = sentence_to_transcript(input_sentence, word_to_code_map) | |
| if transcript_1 is None: | |
| return "Error: Transcript conversion returned None" | |
| numbers = text2int(transcript_1) | |
| if numbers is None: | |
| return "Error: Text to number conversion returned None" | |
| code_to_word_map = {v: k for k, v in word_to_code_map.items()} | |
| text = transcript_to_sentence(numbers, code_to_word_map) | |
| return text | |
| def sel_lng(lng, mic=None, file=None): | |
| if mic is not None: | |
| audio = mic | |
| elif file is not None: | |
| audio = file | |
| else: | |
| return "You must either provide a mic recording or a file" | |
| if lng == "Odiya": | |
| return transcribe_odiya(audio) | |
| elif lng == "Odiya-trans": | |
| return transcribe_odiya_eng(audio) | |
| elif lng == "Hindi-trans": | |
| return transcribe_hin_eng(audio) | |
| elif lng == "Hindi": | |
| return transcribe_hindi(audio) | |
| # Function to replace incorrectly spelled words | |
| def replace_words(sentence): | |
| replacements = [ | |
| (r'\bjiro\b', 'zero'), (r'\bjero\b', 'zero'), (r'\bnn\b', 'one'), | |
| (r'\bn\b', 'one'), (r'\bna\b', 'one'), (r'\btu\b', 'two'), | |
| (r'\btoo\b', 'two'), (r'\bthiri\b', 'three'), (r'\bfor\b', 'four'), | |
| (r'\bfore\b', 'four'), (r'\bfib\b', 'five'), (r'\bdublseven\b', 'double seven'), | |
| (r'\bdubalathri\b', 'double three'), (r'\bnineeit\b', 'nine eight'), | |
| (r'\bfipeit\b', 'five eight'), (r'\bdubal\b', 'double'), (r'\bsevenatu\b', 'seven two'), | |
| ] | |
| for pattern, replacement in replacements: | |
| sentence = re.sub(pattern, replacement, sentence) | |
| return sentence | |
| # Function to process "double" followed by a number | |
| def process_doubles(sentence): | |
| tokens = sentence.split() | |
| result = [] | |
| i = 0 | |
| while i < len(tokens): | |
| if tokens[i] in ("double", "dubal"): | |
| if i + 1 < len(tokens): | |
| result.append(tokens[i + 1]) | |
| result.append(tokens[i + 1]) | |
| i += 2 | |
| else: | |
| result.append(tokens[i]) | |
| i += 1 | |
| else: | |
| result.append(tokens[i]) | |
| i += 1 | |
| return ' '.join(result) | |
| # Function to generate Soundex code for a word | |
| def soundex(word): | |
| word = word.upper() | |
| word = ''.join(filter(str.isalpha, word)) | |
| if not word: | |
| return None | |
| soundex_mapping = { | |
| 'B': '1', 'F': '1', 'P': '1', 'V': '1', | |
| 'C': '2', 'G': '2', 'J': '2', 'K': '2', 'Q': '2', 'S': '2', 'X': '2', 'Z': '2', | |
| 'D': '3', 'T': '3', 'L': '4', 'M': '5', 'N': '5', 'R': '6' | |
| } | |
| soundex_code = word[0] | |
| for char in word[1:]: | |
| if char not in ('H', 'W'): | |
| soundex_code += soundex_mapping.get(char, '0') | |
| soundex_code = soundex_code[0] + ''.join(c for i, c in enumerate(soundex_code[1:]) if c != soundex_code[i]) | |
| soundex_code = soundex_code.replace('0', '') + '000' | |
| return soundex_code[:4] | |
| # Function to convert text to numerical representation | |
| def is_number(x): | |
| if type(x) == str: | |
| x = x.replace(',', '') | |
| try: | |
| float(x) | |
| except: | |
| return False | |
| return True | |
| def text2int(textnum, numwords={}): | |
| units = ['Z600', 'O500','T000','T600','F600','F100','S220','S150','E300','N500', | |
| 'T500', 'E415', 'T410', 'T635', 'F635', 'F135', 'S235', 'S153', 'E235','N535'] | |
| tens = ['', '', 'T537', 'T637', 'F637', 'F137', 'S230', 'S153', 'E230', 'N530'] | |
| scales = ['H536', 'T253', 'M450', 'C600'] | |
| ordinal_words = {'oh': 'Z600', 'first': 'O500', 'second': 'T000', 'third': 'T600', 'fourth': 'F600', 'fifth': 'F100', | |
| 'sixth': 'S200','seventh': 'S150','eighth': 'E230', 'ninth': 'N500', 'twelfth': 'T410'} | |
| ordinal_endings = [('ieth', 'y'), ('th', '')] | |
| if not numwords: | |
| numwords['and'] = (1, 0) | |
| for idx, word in enumerate(units): numwords[word] = (1, idx) | |
| for idx, word in enumerate(tens): numwords[word] = (1, idx * 10) | |
| for idx, word in enumerate(scales): numwords[word] = (10 ** (idx * 3 or 2), 0) | |
| textnum = textnum.replace('-', ' ') | |
| current = result = 0 | |
| curstring = '' | |
| onnumber = False | |
| lastunit = False | |
| lastscale = False | |
| def is_numword(x): | |
| if is_number(x): | |
| return True | |
| if word in numwords: | |
| return True | |
| return False | |
| def from_numword(x): | |
| if is_number(x): | |
| scale = 0 | |
| increment = int(x.replace(',', '')) | |
| return scale, increment | |
| return numwords[x] | |
| for word in textnum.split(): | |
| if word in ordinal_words: | |
| scale, increment = (1, ordinal_words[word]) | |
| current = current * scale + increment | |
| if scale > 100: | |
| result += current | |
| current = 0 | |
| onnumber = True | |
| lastunit = False | |
| lastscale = False | |
| else: | |
| for ending, replacement in ordinal_endings: | |
| if word.endswith(ending): | |
| word = "%s%s" % (word[:-len(ending)], replacement) | |
| if (not is_numword(word)) or (word == 'and' and not lastscale): | |
| if onnumber: | |
| curstring += repr(result + current) + " " | |
| curstring += word + " " | |
| result = current = 0 | |
| onnumber = False | |
| lastunit = False | |
| lastscale = False | |
| else: | |
| scale, increment = from_numword(word) | |
| onnumber = True | |
| if lastunit and (word not in scales): | |
| curstring += repr(result + current) | |
| result = current = 0 | |
| if scale > 1: | |
| current = max(1, current) | |
| current = current * scale + increment | |
| if scale > 100: | |
| result += current | |
| current = 0 | |
| lastscale = False | |
| lastunit = False | |
| if word in scales: | |
| lastscale = True | |
| elif word in units: | |
| lastunit = True | |
| if onnumber: | |
| curstring += repr(result + current) | |
| return curstring | |
| # Convert sentence to transcript using Soundex | |
| def sentence_to_transcript(sentence, word_to_code_map): | |
| words = sentence.split() | |
| transcript_codes = [] | |
| for word in words: | |
| if word not in word_to_code_map: | |
| word_to_code_map[word] = soundex(word) | |
| transcript_codes.append(word_to_code_map[word]) | |
| transcript = ' '.join(transcript_codes) | |
| return transcript | |
| # Convert transcript back to sentence using mapping | |
| def transcript_to_sentence(transcript, code_to_word_map): | |
| codes = transcript.split() | |
| sentence_words = [] | |
| for code in codes: | |
| sentence_words.append(code_to_word_map.get(code, code)) | |
| sentence = ' '.join(sentence_words) | |
| return sentence | |
| # # Process the audio file | |
| # transcript = pipe("./odia_recorded/AUD-20240614-WA0004.wav") | |
| # text_value = transcript['text'] | |
| # sentence = trn.transform(text_value) | |
| # replaced_words = replace_words(sentence) | |
| # processed_sentence = process_doubles(replaced_words) | |
| # input_sentence_1 = processed_sentence | |
| # Create empty mappings | |
| word_to_code_map = {} | |
| code_to_word_map = {} | |
| # Convert sentence to transcript | |
| # transcript_1 = sentence_to_transcript(input_sentence_1, word_to_code_map) | |
| # Convert transcript to numerical representation | |
| # numbers = text2int(transcript_1) | |
| # Create reverse mapping | |
| code_to_word_map = {v: k for k, v in word_to_code_map.items()} | |
| # Convert transcript back to sentence | |
| # reconstructed_sentence_1 = transcript_to_sentence(numbers, code_to_word_map) | |
| # demo=gr.Interface( | |
| # fn=sel_lng, | |
| # inputs=[ | |
| # gr.Dropdown(["Hindi","Hindi-trans","Odiya","Odiya-trans"],value="Hindi",label="Select Language"), | |
| # gr.Audio(source="microphone", type="filepath"), | |
| # gr.Audio(source= "upload", type="filepath"), | |
| # #gr.Audio(sources="upload", type="filepath"), | |
| # #"state" | |
| # ], | |
| # outputs=[ | |
| # "textbox" | |
| # # #"state" | |
| # ], | |
| # title="Automatic Speech Recognition", | |
| # description = "Demo for Automatic Speech Recognition. Use microphone to record speech. Please press Record button. Initially it will take some time to load the model. The recognized text will appear in the output textbox", | |
| # ).launch() | |
| ###################################################### | |
| demo=gr.Interface( | |
| fn=sel_lng, | |
| inputs=[ | |
| gr.Dropdown(["Hindi","Hindi-trans","Odiya","Odiya-trans"],value="Hindi",label="Select Language"), | |
| gr.Audio(sources=["microphone","upload"], type="filepath"), | |
| #gr.Audio(sources="upload", type="filepath"), | |
| #"state" | |
| ], | |
| outputs=[ | |
| "textbox" | |
| # #"state" | |
| ], | |
| allow_flagging="auto", | |
| #flagging_options=["Language error", "English transliteration error", "Other"], | |
| #flagging_callback=hf_writer, | |
| title="Automatic Speech Recognition", | |
| description = "Demo for Automatic Speech Recognition. Use microphone to record speech. Please press Record button. Initially it will take some time to load the model. The recognized text will appear in the output textbox", | |
| ).launch() | |