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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "dc09394e-2130-4bd4-af30-01346d8ee355",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7860\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\gradio\\analytics.py:106: UserWarning: IMPORTANT: You are using gradio version 4.37.2, however version 4.44.1 is available, please upgrade. \n",
"--------\n",
" warnings.warn(\n",
"ERROR: Exception in ASGI application\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\uvicorn\\protocols\\http\\h11_impl.py\", line 404, in run_asgi\n",
" result = await app( # type: ignore[func-returns-value]\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\uvicorn\\middleware\\proxy_headers.py\", line 84, in __call__\n",
" return await self.app(scope, receive, send)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\fastapi\\applications.py\", line 1054, in __call__\n",
" await super().__call__(scope, receive, send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\applications.py\", line 123, in __call__\n",
" await self.middleware_stack(scope, receive, send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\middleware\\errors.py\", line 186, in __call__\n",
" raise exc\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\middleware\\errors.py\", line 164, in __call__\n",
" await self.app(scope, receive, _send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\gradio\\route_utils.py\", line 714, in __call__\n",
" await self.app(scope, receive, send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\middleware\\exceptions.py\", line 62, in __call__\n",
" await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\_exception_handler.py\", line 64, in wrapped_app\n",
" raise exc\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\_exception_handler.py\", line 53, in wrapped_app\n",
" await app(scope, receive, sender)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\routing.py\", line 762, in __call__\n",
" await self.middleware_stack(scope, receive, send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\routing.py\", line 782, in app\n",
" await route.handle(scope, receive, send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\routing.py\", line 297, in handle\n",
" await self.app(scope, receive, send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\routing.py\", line 77, in app\n",
" await wrap_app_handling_exceptions(app, request)(scope, receive, send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\_exception_handler.py\", line 64, in wrapped_app\n",
" raise exc\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\_exception_handler.py\", line 53, in wrapped_app\n",
" await app(scope, receive, sender)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\routing.py\", line 75, in app\n",
" await response(scope, receive, send)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\responses.py\", line 346, in __call__\n",
" await send(\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\_exception_handler.py\", line 50, in sender\n",
" await send(message)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\_exception_handler.py\", line 50, in sender\n",
" await send(message)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\starlette\\middleware\\errors.py\", line 161, in _send\n",
" await send(message)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\uvicorn\\protocols\\http\\h11_impl.py\", line 508, in send\n",
" output = self.conn.send(event=h11.EndOfMessage())\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\h11\\_connection.py\", line 512, in send\n",
" data_list = self.send_with_data_passthrough(event)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\h11\\_connection.py\", line 545, in send_with_data_passthrough\n",
" writer(event, data_list.append)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\h11\\_writers.py\", line 67, in __call__\n",
" self.send_eom(event.headers, write)\n",
" File \"C:\\Users\\WCHL\\anaconda3\\envs\\RunInference2\\Lib\\site-packages\\h11\\_writers.py\", line 96, in send_eom\n",
" raise LocalProtocolError(\"Too little data for declared Content-Length\")\n",
"h11._util.LocalProtocolError: Too little data for declared Content-Length\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"எண்பது\n",
"எண்பது\n",
"எண்பது\n",
"eighty\n",
"80\n"
]
}
],
"source": [
"import gradio as gr\n",
"import librosa\n",
"import numpy as np\n",
"import pywt\n",
"import nbimporter\n",
"from scipy.signal import butter, lfilter, wiener\n",
"from scipy.io.wavfile import write\n",
"from transformers import pipeline\n",
"from text2int import text_to_int\n",
"from isNumber import is_number\n",
"from Text2List import text_to_list\n",
"from convert2list import convert_to_list\n",
"from processDoubles import process_doubles\n",
"from replaceWords import replace_words\n",
"\n",
"asr_model = pipeline(\"automatic-speech-recognition\", model=\"cdactvm/w2v-bert-tamil_new\")\n",
"\n",
"# Function to apply a high-pass filter\n",
"def high_pass_filter(audio, sr, cutoff=300):\n",
" nyquist = 0.5 * sr\n",
" normal_cutoff = cutoff / nyquist\n",
" b, a = butter(1, normal_cutoff, btype='high', analog=False)\n",
" filtered_audio = lfilter(b, a, audio)\n",
" return filtered_audio\n",
"\n",
"# Function to apply wavelet denoising\n",
"def wavelet_denoise(audio, wavelet='db1', level=1):\n",
" coeffs = pywt.wavedec(audio, wavelet, mode='per')\n",
" sigma = np.median(np.abs(coeffs[-level])) / 0.5\n",
" uthresh = sigma * np.sqrt(2 * np.log(len(audio)))\n",
" coeffs[1:] = [pywt.threshold(i, value=uthresh, mode='soft') for i in coeffs[1:]]\n",
" return pywt.waverec(coeffs, wavelet, mode='per')\n",
"\n",
"# Function to apply a Wiener filter for noise reduction\n",
"def apply_wiener_filter(audio):\n",
" return wiener(audio)\n",
"\n",
"# Function to handle speech recognition\n",
"def recognize_speech(audio_file):\n",
" audio, sr = librosa.load(audio_file, sr=16000)\n",
" audio = high_pass_filter(audio, sr)\n",
" audio = apply_wiener_filter(audio)\n",
" denoised_audio = wavelet_denoise(audio)\n",
" result = asr_model(denoised_audio)\n",
" text_value = result['text']\n",
" cleaned_text = text_value.replace(\"<s>\", \"\")\n",
" print(cleaned_text)\n",
" converted_to_list = convert_to_list(cleaned_text, text_to_list())\n",
" print(converted_to_list)\n",
" processed_doubles = process_doubles(converted_to_list)\n",
" print(processed_doubles)\n",
" replaced_words = replace_words(processed_doubles)\n",
" print(replaced_words)\n",
" converted_text = text_to_int(replaced_words)\n",
" print(converted_text)\n",
" return converted_text\n",
"\n",
"# Gradio Interface\n",
"gr.Interface(\n",
" fn=recognize_speech,\n",
" inputs=gr.Audio(sources=[\"microphone\",\"upload\"], type=\"filepath\"),\n",
" outputs=\"text\",\n",
" title=\"Speech Recognition with Advanced Noise Reduction & Hindi ASR\",\n",
" description=\"Upload an audio file, and the system will use high-pass filtering, Wiener filtering, and wavelet-based denoising, then a Hindi ASR model will transcribe the clean audio.\"\n",
").launch()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4565cfb-a8e0-49a1-8878-6e5b1cd105e6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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"name": "python3"
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"codemirror_mode": {
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"file_extension": ".py",
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