app.py

# import warnings
# warnings.filterwarnings("ignore")
# import os
# import re
# import gradio as gr
# import numpy as np
# import torchaudio
# import nbimporter
# from transformers import pipeline
# from transformers import AutoProcessor
# from pyctcdecode import build_ctcdecoder
# from transformers import Wav2Vec2ProcessorWithLM
# from text2int import text_to_int
# from isNumber import is_number
# from Text2List import text_to_list
# from convert2list import convert_to_list
# from processDoubles import process_doubles
# from replaceWords import replace_words

# # transcriber = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-hindi_v1")
# # processor = AutoProcessor.from_pretrained("cdactvm/w2v-bert-2.0-hindi_v1")

# # vocab_dict = processor.tokenizer.get_vocab()

# # sorted_vocab_dict = {k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])}
# # decoder = build_ctcdecoder(
# #     labels=list(sorted_vocab_dict.keys()),
# #     kenlm_model_path="lm.binary",
# #     )
# # processor_with_lm = Wav2Vec2ProcessorWithLM(
# #     feature_extractor=processor.feature_extractor,
# #     tokenizer=processor.tokenizer,
# #     decoder=decoder
# #     )
# # processor.feature_extractor._processor_class = "Wav2Vec2ProcessorWithLM"

 
# def transcribe(audio):
#     # # Process the audio file
#     transcript = transcriber(audio)
#     text_value = transcript['text']
#     print(text_value)
#     processd_doubles=process_doubles(text_value)
#     converted_to_list=convert_to_list(processd_doubles,text_to_list())
#     replaced_words = replace_words(converted_to_list)
#     converted_text=text_to_int(replaced_words)
#     return converted_text


# # demo = gr.Interface(
# #     transcribe,
# #     gr.Audio(sources="microphone", type="filepath"),
# #     "text",
# # )

# # demo.launch()

# demo=gr.Interface(
#     transcribe,
#     inputs=[
#         gr.Audio(sources=["microphone","upload"], type="filepath"),
#     ],
#     outputs=[
#         "textbox"
#     ],
#     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()

###############################################################
import warnings
warnings.filterwarnings("ignore")
import os
import re
import gradio as gr
import numpy as np
import torchaudio
import nbimporter
from transformers import pipeline
from transformers import AutoProcessor
from pyctcdecode import build_ctcdecoder
from transformers import Wav2Vec2ProcessorWithLM
from text2int import text_to_int
from isNumber import is_number
from Text2List import text_to_list
from convert2list import convert_to_list
from processDoubles import process_doubles
from replaceWords import replace_words

hindi_model = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-hindi_v1")
import warnings
import gradio as gr
from transformers import pipeline
from transformers import AutoProcessor
from pyctcdecode import build_ctcdecoder
from transformers import Wav2Vec2ProcessorWithLM

import os
import re
#import torchaudio

# Initialize the speech recognition pipeline and transliterator
odia_model1 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-odia_v1")
odia_model2 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-odia_v2")
# p2 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-hindi_v1")
# punjaib_modle_30000=pipeline(task="automatic-speech-recognition", model="cdactvm/wav2vec-bert-punjabi-30000-model")
# punjaib_modle_155750=pipeline(task="automatic-speech-recognition", model="cdactvm/wav2vec-bert-punjabi-155750-model")
# punjaib_modle_70000_aug=pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-model-30000-augmented")
#p3 = pipeline(task="automatic-speech-recognition", model="cdactvm/kannada_w2v-bert_model")
#p4 = pipeline(task="automatic-speech-recognition", model="cdactvm/telugu_w2v-bert_model")
#p5 = pipeline(task="automatic-speech-recognition", model="Sajjo/w2v-bert-2.0-bangala-gpu-CV16.0_v2")
#p6 = pipeline(task="automatic-speech-recognition", model="cdactvm/hf-open-assames")
# p7 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-assames")
processor = AutoProcessor.from_pretrained("cdactvm/w2v-bert-odia_v2")
vocab_dict = processor.tokenizer.get_vocab()
sorted_vocab_dict = {k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])}
decoder = build_ctcdecoder(
    labels=list(sorted_vocab_dict.keys()),
    kenlm_model_path="lm.binary",
    )
processor_with_lm = Wav2Vec2ProcessorWithLM(
    feature_extractor=processor.feature_extractor,
    tokenizer=processor.tokenizer,
    decoder=decoder
    )
processor.feature_extractor._processor_class = "Wav2Vec2ProcessorWithLM"
#p8 = pipeline("automatic-speech-recognition", model="cdactvm/w2v-assames", tokenizer=processor_with_lm, feature_extractor=processor_with_lm.feature_extractor, decoder=processor_with_lm.decoder)
    

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

###########################################

# 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'\bvan\b', 'one'),(r'\bna\b', 'one'), (r'\bnn\b', 'one'),(r'\bek\b', 'one'),
        (r'\btu\b', 'two'),(r'\btoo\b', 'two'),(r'\bdo\b', 'two'),
        (r'\bthiri\b', 'three'), (r'\btiri\b', 'three'), (r'\bdubalathri\b', 'double three'),(r'\btin\b', 'three'), 
        (r'\bfor\b', 'four'),(r'\bfore\b', 'four'), 
        (r'\bfib\b', 'five'),(r'\bpaanch\b', 'five'), 
        (r'\bchha\b', 'six'),(r'\bchhah\b', 'six'),(r'\bchau\b', 'six'),
        (r'\bdublseven\b', 'double seven'),(r'\bsath\b', 'seven'),
        (r'\baath\b', 'eight'),
        (r'\bnau\b', 'nine'),
        (r'\bdas\b', 'ten'),
        (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 x 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()}

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 transcribe_punjabi_30000(speech):
    text = punjaib_modle_30000(speech)["text"]
    text = text.replace("[PAD]","")
    if text is None:
        return "Error: ASR returned None"
    return text

def transcribe_punjabi_eng_model_30000(speech):
    trn = Transliterator(source='pan', target='eng', build_lookup=True)
    text = punjaib_modle_30000(speech)["text"]
    text = text.replace("[PAD]","")
    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)
    return sentence

def transcribe_punjabi_70000_aug(speech):
    text = punjaib_modle_70000_aug(speech)["text"]
    text = text.replace("<s>","")
    if text is None:
        return "Error: ASR returned None"
    return text

def transcribe_punjabi_eng_model_70000_aug(speech):
    trn = Transliterator(source='pan', target='eng', build_lookup=True)
    text = punjaib_modle_70000_aug(speech)["text"]
    text = text.replace("<s>","")
    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)
    return sentence

def transcribe_punjabi_155750(speech):
    text = punjaib_modle_155750(speech)["text"]
    text = text.replace("[PAD]","")
    if text is None:
        return "Error: ASR returned None"
    return text

def transcribe_punjabi_eng_model_155750(speech):
    trn = Transliterator(source='pan', target='eng', build_lookup=True)
    text = punjaib_modle_155750(speech)["text"]
    text = text.replace("[PAD]","")
    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)
    return sentence
    
###########################################
def transcribe_odiya_model1(speech):
    text = odia_model1(speech)["text"]
    if text is None:
        return "Error: ASR returned None"
    return text

def transcribe_odiya_model2(speech):
    text = odia_model2(speech)["text"]
    if text is None:
        return "Error: ASR returned None"
    return text

def transcribe_odiya_eng_model1(speech):
    trn = Transliterator(source='ori', target='eng', build_lookup=True)
    text = odia_model1(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_odiya_eng_model2(speech):
    trn = Transliterator(source='ori', target='eng', build_lookup=True)
    text = odia_model2(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 cleanhtml(raw_html):
    cleantext = re.sub(r'<.*?>', '', raw_html)
    return cleantext
#######################################

# def transcribe_hindi(speech):
#     text = p2(speech)["text"]
#     if text is None:
#         return "Error: ASR returned None"
#     return text

def transcribe_hindi(speech):
    text = hindi_model(speech)["text"]
    if text is None:
        return "Error: ASR returned None"

    hindi_map = {   
        "सेवन": "7",
        "जीरो": "0",
        "वन" : "1",
        "टू"  : "2",
        "थ्री"  : "3",
        "त्री"  : "3",
        "फोर" : "4",
        "फाइव": "5",
        "सिक्स": "6",
        "एट": "8",
        "नाइन": "9",
        "टेन": "10",
        "एक": "1",
        "दो": "2",
        "तीन": "3",
        "चार": "4",
        "पांच": "5",
        "पाँच": "5",
        "छह": "6",
        "छः": "6", 
        "सात": "7",
        "आठ": "8",
        "नौ": "9",
        "दस": "10"
    }
    
    for hindi, num in hindi_map.items():
        text = text.replace(hindi, num)

    # Split the string into parts separated by spaces
    parts = text.split(' ')

    # Initialize an empty list to store the processed parts
    processed_parts = []

    # Iterate over each part
    for part in parts:
        # Check if the part is a number (contains only digits)
        if part.isdigit():
            # If the previous part was also a number, concatenate them
            if processed_parts and processed_parts[-1].isdigit():
                processed_parts[-1] += part
            else:
                processed_parts.append(part)
        else:
            # If the part is not a number, add it to the list as is
            processed_parts.append(part)

    # Join the processed parts back into a string with spaces
    text = ' '.join(processed_parts)

    return text

###########################################################
def transcribe_kannada(speech):
    text = p3(speech)["text"]
    if text is None:
        return "Error: ASR returned None"
    return text
def transcribe_telugu(speech):
    text = p4(speech)["text"]
    if text is None:
        return "Error: ASR returned None"
    return text
    
def transcribe_bangala(speech):
    text = p5(speech)["text"]
    if text is None:
        return "Error: ASR returned None"
    return text

def transcribe_assamese_LM(speech):
    text = p8(speech)["text"]
    text = cleanhtml(text)
    if text is None:
        return "Error: ASR returned None"
    return text
    
def transcribe_assamese_model2(speech):
    text = p7(speech)["text"]
    text = cleanhtml(text)
    if text is None:
        return "Error: ASR returned None"
    return text
    
def transcribe_ban_eng(speech):
    trn = Transliterator(source='ben', target='eng', build_lookup=True)
    text = p5(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 transcribe_kan_eng(speech):
    trn = Transliterator(source='kan', target='eng', build_lookup=True)
    text = p3(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_tel_eng(speech):
    trn = Transliterator(source='tel', target='eng', build_lookup=True)
    text = p4(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 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)
    elif lng == "Kannada-trans":
       return transcribe_kan_eng(audio)
    elif lng == "Kannada":
       return transcribe_kannada(audio)
    elif lng == "Telugu-trans":
       return transcribe_tel_eng(audio)
    elif lng == "Telugu":
       return transcribe_telugu(audio)
    elif lng == "Bangala-trans":
       return transcribe_ban_eng(audio)
    elif lng == "Bangala":
       return transcribe_bangala(audio)
    elif lng == "Assamese-LM":
       return transcribe_assamese_LM(audio)
    elif lng == "Assamese-Model2":
       return transcribe_assamese_model2(audio)
    elif lng == "Odia_model1":
       return transcribe_odiya_model1(audio)
    elif lng == "Odiya_trans_model1":
       return transcribe_odiya_eng_model1(audio)
    elif lng == "Odia_model2":
       return transcribe_odiya_model2(audio)
    elif lng == "Odia_trans_model2":
       return transcribe_odiya_eng_model2(audio)
    elif lng == "Punjabi_Model0":
       return transcribe_punjabi_30000(audio)
    elif lng == "Punjabi_Model0_Trans":
       return transcribe_punjabi_eng_model_30000(audio)
    elif lng == "Punjabi_Model_aug":
       return transcribe_punjabi_70000_aug(audio)
    elif lng == "Punjabi_Model_aug_Trans":
       return transcribe_punjabi_eng_model_70000_aug(audio)
    elif lng == "Punjabi_Model1":
       return transcribe_punjabi_155750(audio)
    elif lng == "Punjabi_Model1_Trans":
       return transcribe_punjabi_eng_model_155750(audio)
        
    


# 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","Kannada","Kannada-trans","Telugu","Telugu-trans","Bangala","Bangala-trans"],value="Hindi",label="Select Language"),
        gr.Dropdown([
            # "Hindi","Hindi-trans",
            "Odia_model1","Odiya_trans_model1","Odia_model2","Odia_trans_model2"],label="Select Language"),
            # "Assamese-LM","Assamese-Model2",
            # "Punjabi_Model1","Punjabi_Model1_Trans","Punjabi_Model_aug","Punjabi_Model_aug_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()