{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "kosakhNmxb7A" }, "source": [ "## Install the Whisper Code" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331, "referenced_widgets": [ "e3cc7ea530c9409182e1087e9fe50500", "c1b3497b05c944ccaa8fcabec916c91c", "e56801d65c50457da59df7f671c715a5", "3e187be29c8148adb24fdcf920b7c3dc", "1e1a94eda41b4625965d43bab9890e5b", "b58d287b6ff3476fbeddd140381da886", "0cffb6cf9e2447338c321beb6b3c8d5f", "bed24dfe60df45f8ae7735e9943e41fd", "86f4b33b39a646dabf5269fe842e95e8", "52a01d6c29924f8fb089d7c1313727ae", "e4ddba34a7c3453b844c52e03e32cf04", "cc29e566671843e2958dfbcfbd545092", "57567346c52c4858a169794fa7f60b37", "eea9a4597bec40d986f1e2b8ca227913", "76a480e0e84142a28e2e25d136fda440", "01bfe2463222446e97a8ddc958e85d19", "712841cc87bf44728f00741c0d722e1f" ] }, "id": "HD6taqcxUC-Y", "outputId": "d79ba02a-7fec-4d61-cceb-e219cca03e8e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", " \n", " A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n", " Setting a new token will erase the existing one.\n", " To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n", "Token can be pasted using 'Right-Click'.\n", "Token: ········\n", "Add token as git credential? (Y/n) y\n", "Token is valid.\n", "Your token has been saved in your configured git credential helpers (manager).\n", "Your token has been saved to C:\\Users\\prlab\\.cache\\huggingface\\token\n", "Login successful\n" ] } ], "source": [ "from huggingface_hub import interpreter_login\n", "\n", "interpreter_login()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "827gUrY4UE-_" }, "outputs": [], "source": [ "whisper_model = \"openai/whisper-small\"\n", "language = \"ko\"\n", "dataset_name = \"whisper-data\"\t# huggingface hub에 저장할 데이터세트 이름\n", "model_name = \"whisper_fine_tuning_lr_30000\"\t# huggingface hub에 저장할 모델 이름\n", "huggingface_name = \"yd97\"\t# huggingface hub의 닉네임" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "bjDx7ip30C-1" }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "id": "hGGwhK5e0C-1", "outputId": "a5d5602b-0d41-4ba7-de49-34e7ec9c3463" }, "outputs": [ { "data": { "text/html": [ "
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pathsentence
0./audio_segmentation/wav/Arete-0001_52_55.wav식사
1./audio_segmentation/wav/Arete-0001_55_57.wav어떤 때는 계란 두 개 먹고
2./audio_segmentation/wav/Arete-0001_58_64.wav저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고
3./audio_segmentation/wav/Arete-0001_65_67.wav아니면 밥 조금 더 곁들일 때도 있고
4./audio_segmentation/wav/Arete-0001_71_75.wav아니 아저씨랑 먹을 때도 있고
.........
20740./audio_segmentation/wav/M_YGT_73_389_392.wav마을에서 쫓아내려고 했습니다
20741./audio_segmentation/wav/M_YGT_73_397_402.wav착한 콩쥐는 팥쥐와 팥쥐엄마를
20742./audio_segmentation/wav/M_YGT_73_406_409.wav집에 데리고 와 함께 살았습니다
20743./audio_segmentation/wav/M_YGT_73_416_420.wav사또는 콩쥐가 착한 했다는 소문을 듣고
20744./audio_segmentation/wav/M_YGT_73_424_427.wav콩쥐를 신부로 맞이하였습니다
\n", "

20745 rows × 2 columns

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" ], "text/plain": [ " path \\\n", "0 ./audio_segmentation/wav/Arete-0001_52_55.wav \n", "1 ./audio_segmentation/wav/Arete-0001_55_57.wav \n", "2 ./audio_segmentation/wav/Arete-0001_58_64.wav \n", "3 ./audio_segmentation/wav/Arete-0001_65_67.wav \n", "4 ./audio_segmentation/wav/Arete-0001_71_75.wav \n", "... ... \n", "20740 ./audio_segmentation/wav/M_YGT_73_389_392.wav \n", "20741 ./audio_segmentation/wav/M_YGT_73_397_402.wav \n", "20742 ./audio_segmentation/wav/M_YGT_73_406_409.wav \n", "20743 ./audio_segmentation/wav/M_YGT_73_416_420.wav \n", "20744 ./audio_segmentation/wav/M_YGT_73_424_427.wav \n", "\n", " sentence \n", "0 식사 \n", "1 어떤 때는 계란 두 개 먹고 \n", "2 저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고 \n", "3 아니면 밥 조금 더 곁들일 때도 있고 \n", "4 아니 아저씨랑 먹을 때도 있고 \n", "... ... \n", "20740 마을에서 쫓아내려고 했습니다 \n", "20741 착한 콩쥐는 팥쥐와 팥쥐엄마를 \n", "20742 집에 데리고 와 함께 살았습니다 \n", "20743 사또는 콩쥐가 착한 했다는 소문을 듣고 \n", "20744 콩쥐를 신부로 맞이하였습니다 \n", "\n", "[20745 rows x 2 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df = pd.read_csv(\"E:/Emocog/emocog_alzheimer_lingustic/linguistic_data.csv\")\n", "train_df=train_df.drop(['wav_length'],axis=1)\n", "train_df" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "id": "risGQW__04yD", "outputId": "a69456d9-c610-45a6-c5a4-2880347cfc20" }, "outputs": [ { "data": { "text/html": [ "
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audiotranscription
0./audio_segmentation/wav/Arete-0001_52_55.wav식사
1./audio_segmentation/wav/Arete-0001_55_57.wav어떤 때는 계란 두 개 먹고
2./audio_segmentation/wav/Arete-0001_58_64.wav저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고
3./audio_segmentation/wav/Arete-0001_65_67.wav아니면 밥 조금 더 곁들일 때도 있고
4./audio_segmentation/wav/Arete-0001_71_75.wav아니 아저씨랑 먹을 때도 있고
.........
20740./audio_segmentation/wav/M_YGT_73_389_392.wav마을에서 쫓아내려고 했습니다
20741./audio_segmentation/wav/M_YGT_73_397_402.wav착한 콩쥐는 팥쥐와 팥쥐엄마를
20742./audio_segmentation/wav/M_YGT_73_406_409.wav집에 데리고 와 함께 살았습니다
20743./audio_segmentation/wav/M_YGT_73_416_420.wav사또는 콩쥐가 착한 했다는 소문을 듣고
20744./audio_segmentation/wav/M_YGT_73_424_427.wav콩쥐를 신부로 맞이하였습니다
\n", "

20745 rows × 2 columns

\n", "
" ], "text/plain": [ " audio \\\n", "0 ./audio_segmentation/wav/Arete-0001_52_55.wav \n", "1 ./audio_segmentation/wav/Arete-0001_55_57.wav \n", "2 ./audio_segmentation/wav/Arete-0001_58_64.wav \n", "3 ./audio_segmentation/wav/Arete-0001_65_67.wav \n", "4 ./audio_segmentation/wav/Arete-0001_71_75.wav \n", "... ... \n", "20740 ./audio_segmentation/wav/M_YGT_73_389_392.wav \n", "20741 ./audio_segmentation/wav/M_YGT_73_397_402.wav \n", "20742 ./audio_segmentation/wav/M_YGT_73_406_409.wav \n", "20743 ./audio_segmentation/wav/M_YGT_73_416_420.wav \n", "20744 ./audio_segmentation/wav/M_YGT_73_424_427.wav \n", "\n", " transcription \n", "0 식사 \n", "1 어떤 때는 계란 두 개 먹고 \n", "2 저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고 \n", "3 아니면 밥 조금 더 곁들일 때도 있고 \n", "4 아니 아저씨랑 먹을 때도 있고 \n", "... ... \n", "20740 마을에서 쫓아내려고 했습니다 \n", "20741 착한 콩쥐는 팥쥐와 팥쥐엄마를 \n", "20742 집에 데리고 와 함께 살았습니다 \n", "20743 사또는 콩쥐가 착한 했다는 소문을 듣고 \n", "20744 콩쥐를 신부로 맞이하였습니다 \n", "\n", "[20745 rows x 2 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.columns=['audio','transcription']\n", "train_df" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "id": "r04sAKIg1ivo", "outputId": "3a19cc54-8fd1-429e-c5d9-5915c4b3ed96" }, "outputs": [ { "data": { "text/html": [ "
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audiotranscription
0E:/Emocog/emocog_alzheimer_lingustic/audio_seg...식사
1E:/Emocog/emocog_alzheimer_lingustic/audio_seg...어떤 때는 계란 두 개 먹고
2E:/Emocog/emocog_alzheimer_lingustic/audio_seg...저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고
3E:/Emocog/emocog_alzheimer_lingustic/audio_seg...아니면 밥 조금 더 곁들일 때도 있고
4E:/Emocog/emocog_alzheimer_lingustic/audio_seg...아니 아저씨랑 먹을 때도 있고
.........
20740E:/Emocog/emocog_alzheimer_lingustic/audio_seg...마을에서 쫓아내려고 했습니다
20741E:/Emocog/emocog_alzheimer_lingustic/audio_seg...착한 콩쥐는 팥쥐와 팥쥐엄마를
20742E:/Emocog/emocog_alzheimer_lingustic/audio_seg...집에 데리고 와 함께 살았습니다
20743E:/Emocog/emocog_alzheimer_lingustic/audio_seg...사또는 콩쥐가 착한 했다는 소문을 듣고
20744E:/Emocog/emocog_alzheimer_lingustic/audio_seg...콩쥐를 신부로 맞이하였습니다
\n", "

20745 rows × 2 columns

\n", "
" ], "text/plain": [ " audio \\\n", "0 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "1 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "2 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "3 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "4 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "... ... \n", "20740 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20741 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20742 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20743 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20744 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "\n", " transcription \n", "0 식사 \n", "1 어떤 때는 계란 두 개 먹고 \n", "2 저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고 \n", "3 아니면 밥 조금 더 곁들일 때도 있고 \n", "4 아니 아저씨랑 먹을 때도 있고 \n", "... ... \n", "20740 마을에서 쫓아내려고 했습니다 \n", "20741 착한 콩쥐는 팥쥐와 팥쥐엄마를 \n", "20742 집에 데리고 와 함께 살았습니다 \n", "20743 사또는 콩쥐가 착한 했다는 소문을 듣고 \n", "20744 콩쥐를 신부로 맞이하였습니다 \n", "\n", "[20745 rows x 2 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df['audio']=train_df['audio'].apply(lambda x:'E:/Emocog/emocog_alzheimer_lingustic/audio_segmentation'+x[1:])\n", "train_df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WiLjuU4s2g73", "outputId": "d05bfcab-d9bd-4f7d-f31c-fb96d0faea47" }, "outputs": [ { "data": { "text/plain": [ "0 식사\n", "1 어떤 때는 계란 두 개 먹고\n", "2 저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고\n", "3 아니면 밥 조금 더 곁들일 때도 있고\n", "4 아니 아저씨랑 먹을 때도 있고\n", " ... \n", "20740 마을에서 쫓아내려고 했습니다\n", "20741 착한 콩쥐는 팥쥐와 팥쥐엄마를\n", "20742 집에 데리고 와 함께 살았습니다\n", "20743 사또는 콩쥐가 착한 했다는 소문을 듣고\n", "20744 콩쥐를 신부로 맞이하였습니다\n", "Name: transcription, Length: 20745, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df['transcription']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4kTs8EWb34_4", "outputId": "ced24250-8dbb-44a6-b3c4-4612e0c2dac1" }, "outputs": [ { "data": { "text/plain": [ "0 식사\n", "1 어떤 때는 계란 두 개 먹고\n", "2 저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고\n", "3 아니면 밥 조금 더 곁들일 때도 있고\n", "4 아니 아저씨랑 먹을 때도 있고\n", " ... \n", "20740 마을에서 쫓아내려고 했습니다\n", "20741 착한 콩쥐는 팥쥐와 팥쥐엄마를\n", "20742 집에 데리고 와 함께 살았습니다\n", "20743 사또는 콩쥐가 착한 했다는 소문을 듣고\n", "20744 콩쥐를 신부로 맞이하였습니다\n", "Name: transcription, Length: 20745, dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df['transcription']=train_df['transcription'].astype(str)\n", "train_df['transcription']" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "id": "Delmo8Zb0C-2", "outputId": "671ba2bb-9605-4d9f-c467-a45a894bffa6" }, "outputs": [ { "data": { "text/html": [ "
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audiotranscription
0E:/Emocog/emocog_alzheimer_lingustic/audio_seg...식사
1E:/Emocog/emocog_alzheimer_lingustic/audio_seg...어떤 때는 계란 두 개 먹고
2E:/Emocog/emocog_alzheimer_lingustic/audio_seg...저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고
3E:/Emocog/emocog_alzheimer_lingustic/audio_seg...아니면 밥 조금 더 곁들일 때도 있고
4E:/Emocog/emocog_alzheimer_lingustic/audio_seg...아니 아저씨랑 먹을 때도 있고
.........
20740E:/Emocog/emocog_alzheimer_lingustic/audio_seg...마을에서 쫓아내려고 했습니다
20741E:/Emocog/emocog_alzheimer_lingustic/audio_seg...착한 콩쥐는 팥쥐와 팥쥐엄마를
20742E:/Emocog/emocog_alzheimer_lingustic/audio_seg...집에 데리고 와 함께 살았습니다
20743E:/Emocog/emocog_alzheimer_lingustic/audio_seg...사또는 콩쥐가 착한 했다는 소문을 듣고
20744E:/Emocog/emocog_alzheimer_lingustic/audio_seg...콩쥐를 신부로 맞이하였습니다
\n", "

20745 rows × 2 columns

\n", "
" ], "text/plain": [ " audio \\\n", "0 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "1 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "2 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "3 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "4 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "... ... \n", "20740 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20741 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20742 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20743 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20744 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "\n", " transcription \n", "0 식사 \n", "1 어떤 때는 계란 두 개 먹고 \n", "2 저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고 \n", "3 아니면 밥 조금 더 곁들일 때도 있고 \n", "4 아니 아저씨랑 먹을 때도 있고 \n", "... ... \n", "20740 마을에서 쫓아내려고 했습니다 \n", "20741 착한 콩쥐는 팥쥐와 팥쥐엄마를 \n", "20742 집에 데리고 와 함께 살았습니다 \n", "20743 사또는 콩쥐가 착한 했다는 소문을 듣고 \n", "20744 콩쥐를 신부로 맞이하였습니다 \n", "\n", "[20745 rows x 2 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import re\n", "def clean_text(text):\n", " cleaned_text=re.sub(r\"[^\\uAC00-\\uD7A30-9\\s]\", \"\", text)\n", " return cleaned_text\n", "\n", "for i in range(len(train_df)):\n", " train_df['transcription'][i] = clean_text(train_df['transcription'][i])\n", "train_df\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[90, 434, 443, 651, 861, 923, 924, 1062, 1218, 1635, 2357, 2781, 4113, 4593, 4761, 5313, 6842, 6854, 6855, 6856, 6860, 6893, 7019, 7023, 7048, 7162, 7330, 7339, 7477, 7738, 7822, 7845, 8153, 11074, 12616, 12825, 12833, 12855, 12856, 12883, 12948, 13030, 13427, 13428, 13490, 13686, 14360, 14363, 16240, 17117, 18093, 18228, 18864, 18879, 19356, 20036, 20081, 20574, 20595, 20596]\n", "60\n" ] } ], "source": [ "a=[]\n", "for i in range(len(train_df)):\n", " if len(train_df['transcription'][i])==0:\n", " a.append(i)\n", "print(a)\n", "print(len(a))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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audiotranscription
0E:/Emocog/emocog_alzheimer_lingustic/audio_seg...식사
1E:/Emocog/emocog_alzheimer_lingustic/audio_seg...어떤 때는 계란 두 개 먹고
2E:/Emocog/emocog_alzheimer_lingustic/audio_seg...저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고
3E:/Emocog/emocog_alzheimer_lingustic/audio_seg...아니면 밥 조금 더 곁들일 때도 있고
4E:/Emocog/emocog_alzheimer_lingustic/audio_seg...아니 아저씨랑 먹을 때도 있고
.........
20680E:/Emocog/emocog_alzheimer_lingustic/audio_seg...마을에서 쫓아내려고 했습니다
20681E:/Emocog/emocog_alzheimer_lingustic/audio_seg...착한 콩쥐는 팥쥐와 팥쥐엄마를
20682E:/Emocog/emocog_alzheimer_lingustic/audio_seg...집에 데리고 와 함께 살았습니다
20683E:/Emocog/emocog_alzheimer_lingustic/audio_seg...사또는 콩쥐가 착한 했다는 소문을 듣고
20684E:/Emocog/emocog_alzheimer_lingustic/audio_seg...콩쥐를 신부로 맞이하였습니다
\n", "

20685 rows × 2 columns

\n", "
" ], "text/plain": [ " audio \\\n", "0 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "1 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "2 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "3 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "4 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "... ... \n", "20680 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20681 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20682 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20683 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "20684 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "\n", " transcription \n", "0 식사 \n", "1 어떤 때는 계란 두 개 먹고 \n", "2 저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고 \n", "3 아니면 밥 조금 더 곁들일 때도 있고 \n", "4 아니 아저씨랑 먹을 때도 있고 \n", "... ... \n", "20680 마을에서 쫓아내려고 했습니다 \n", "20681 착한 콩쥐는 팥쥐와 팥쥐엄마를 \n", "20682 집에 데리고 와 함께 살았습니다 \n", "20683 사또는 콩쥐가 착한 했다는 소문을 듣고 \n", "20684 콩쥐를 신부로 맞이하였습니다 \n", "\n", "[20685 rows x 2 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df=train_df.drop(a,axis=0)\n", "train_df=train_df.reset_index(drop=True)\n", "train_df" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "moEv2tQv5KBJ" }, "outputs": [], "source": [ "a=train_df" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "id": "3FtvxjBw4w1r", "outputId": "c4b6c2c1-0e64-4906-8cb6-175a852f643f" }, "outputs": [ { "data": { "text/html": [ "
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audiotranscription
0E:/Emocog/emocog_alzheimer_lingustic/audio_seg...식사
1E:/Emocog/emocog_alzheimer_lingustic/audio_seg...어떤 때는 계란 두 개 먹고
2E:/Emocog/emocog_alzheimer_lingustic/audio_seg...저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고
3E:/Emocog/emocog_alzheimer_lingustic/audio_seg...아니면 밥 조금 더 곁들일 때도 있고
4E:/Emocog/emocog_alzheimer_lingustic/audio_seg...아니 아저씨랑 먹을 때도 있고
.........
4132E:/Emocog/emocog_alzheimer_lingustic/audio_seg...저 저기 그리고 밥 먹고 나서 꼭 저기 커피 한 잔에다가 그거 먹고 위에다가 저기 ...
4133E:/Emocog/emocog_alzheimer_lingustic/audio_seg...텔레비전 저 딱 하 하루에 오전에 두 시간 오후에는 이제 저 이제 지금 이제 텔레비...
4134E:/Emocog/emocog_alzheimer_lingustic/audio_seg...저 이제 뉴스 하고요 저기 노래 나오는 거
4135E:/Emocog/emocog_alzheimer_lingustic/audio_seg...아 기분은 뭐 이렇게 매 이렇게 날씨 좋으면 좋고 날 날씨가 우중충하면 내 마음도...
4136E:/Emocog/emocog_alzheimer_lingustic/audio_seg...오늘 날씨 좋잖아요
\n", "

4137 rows × 2 columns

\n", "
" ], "text/plain": [ " audio \\\n", "0 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "1 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "2 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "3 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "4 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "... ... \n", "4132 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "4133 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "4134 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "4135 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "4136 E:/Emocog/emocog_alzheimer_lingustic/audio_seg... \n", "\n", " transcription \n", "0 식사 \n", "1 어떤 때는 계란 두 개 먹고 \n", "2 저기 토마토하고 우유하고 뭐 아로니에 하고 이런 거 갈아서 마시고 밥 지을 때도 있고 \n", "3 아니면 밥 조금 더 곁들일 때도 있고 \n", "4 아니 아저씨랑 먹을 때도 있고 \n", "... ... \n", "4132 저 저기 그리고 밥 먹고 나서 꼭 저기 커피 한 잔에다가 그거 먹고 위에다가 저기 ... \n", "4133 텔레비전 저 딱 하 하루에 오전에 두 시간 오후에는 이제 저 이제 지금 이제 텔레비... \n", "4134 저 이제 뉴스 하고요 저기 노래 나오는 거 \n", "4135 아 기분은 뭐 이렇게 매 이렇게 날씨 좋으면 좋고 날 날씨가 우중충하면 내 마음도... \n", "4136 오늘 날씨 좋잖아요 \n", "\n", "[4137 rows x 2 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df=a[:16548]\n", "test_df=a[:-16548]\n", "test_df" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "S3yJIV1s0C-4" }, "outputs": [], "source": [ "train_df=train_df.reset_index(drop=True)\n", "test_df=test_df.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "RJMe5DGSUYvX" }, "outputs": [], "source": [ "from datasets import DatasetDict, Dataset, Audio\n", "import pandas as pd\n", "import json\n", "\n", "\n", "train_dataset = Dataset.from_pandas(train_df)\n", "test_dataset = Dataset.from_pandas(test_df)\n", "\n", "dataset = DatasetDict()\n", "\n", "dataset[\"train\"] = train_dataset\n", "dataset[\"test\"] = test_dataset" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0VBiGfgwka3q", "outputId": "ac8b2678-dc36-4b8c-b423-45e4002283df" }, "outputs": [ { "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['audio', 'transcription'],\n", " num_rows: 16548\n", " })\n", " test: Dataset({\n", " features: ['audio', 'transcription'],\n", " num_rows: 4137\n", " })\n", "})" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 312, "referenced_widgets": [ "b3dd6052433b4bf4a7fc69226be55169", "f592b7d6ca1c4e53a8a60df0918d9ecc", "91ffcb35e84f48eeac68060b8df32239", "d8b4f567689a48928de10360051fd3e5", "49e9fd8346ec44a3a6f87f14b6451be7", "476423dac4904df781b24ec8b6fe1b94", "c1932f066f4a4c738d8caa8eefc66de2", "7640ecbb93a347e6aef6211bc64c3c7f", "b3802a8c856e42048963e48ca4e8612f", "4cac7b39bdf24e4fa4926728c7abc774", "aa397b65cdc84e7b9584f36c9d9a875c", "3fa69ab5a2394971896d2b187e9f8a33", "f3c70b099fbe4b6a96c5b1228e3aae8f", "e33595f5d2f9401eb4b38a1c215fe1eb", "35722ea7d5ec466fb598815e73200c38", "55a0ec5e5e2341518f3e56362750818d", "2ebda20a30f64c5ba4ac4a99cf6d22e0", "cb282414cf004e218f4fd27b00b7a24a", "1b61f113bdcc40acbe2bdec0a3f43c7f", "2575963b33334afd8acd3673d9d00986", "eefd63d2cbd44250bc49df43b1df42df", "3223ee6121c14dfd899bd5ad0476ae08", "70bc0229f38543d7abdcfb58a360cb4e", "3e2b12c4d8e44d5a9cc8c38d9212f2f9", "82359cf26efc47479e257b190de06f36", "41a3133885274c15a399916837364e17", "01ac97405aa3469b9185f01fa5311ad8", "a1cc872213304501b1191fa8961bba4b", "e460cdfda332401f97359a74d70d0241", "868c93568d9f454f95c6abf6ebcfcd7a", "58a2c2436fb9477ea24280ea9ba60901", "93741ec2be574bdb99be8365d854319b", "02eb775a18d1403583c92da2422ac0c7", "2f800fcee5194846b27f182f866b1ebe", "2858f9b975e9429fad326c8bb0f0b68b", "37a4f3b1cbdd45d3af70975f85dd011f", "a6b4c3c09c3544ee9cc8eef199cd2793", "53a37b74c2ad48668c625949b83b86be", "62476836cee24856a551f7b450b1be40", "33f56a7f1ef043b0abf3d30b748e0dd8", "0741dc3bb8e04aa39bfc65f828fd3ffd", "f6260834ff734656b450ad621ee1f117", "4d466fc416774c42a569688a90191c78", "e850a3761ea949d9817b0b8e73aed306", "92d2533b9b45496d93b53b005e48f1c4", "3f5c2eb92ed34d268018767d42fcce7f", "5766749a49a7465e873fb999ac7e2e38", "11c7b50ad75445f383ed6c73b05efbcd", "da91b64f283e448f8560cf65741dcda8", "8caa423e5143412fa25c6336e511c7d4", "093db728909b4a8ca9f1a0ad115491f5", "827f382ce28e40ee9b66c5596e91cbfc", "77485508927b45ec8812592b2b94b89b", "84af72c2cbf0442abd928a25da3bf11f", "41571ee6fe744fc1821c55ba3daaa4de", "67ffa83528504bf3918433b511e2ba01", "a1ef5734763c4c22bb3b7d4de8f12095", "108b088c330e47fba7105de0b80a5d7f", "2a01ec3f08f843d4bf7b7e5b287019ac", "6899cc89b6f94228bf6be3ae732d34ea", "e88944eeb249437798da81702c140ae6", "1685ca937aa64dee9d4800d3cf2044f5", "54a1cc741f75481484dd3576c4d3006a", "e64842dc9fbb4e9da58d512c9400c353", "a0cb55e0e5fe4c4dbe96042341f1f2f8", "31638e26c7364a71983ea1e7618e9b90", "a55df342cb9b4f1e84937c87f0bf9b1f", "e000ca9a560c4cc9869e4e0d9e77b814", "b6d4718509874c569a84fd621ddbf8f0", "ccb48244d6e541dd83822a87380a32ce", "ee864b02c18e4f16851c39da034ef051", "ab8a7aed5f604c0a8c4629060712ce8d", "7c32d2b65b8b4d25878d558f4cc8f45f", "88efb1fa68434f06bee872c931da0d6b", "006dde0de65c4def88a26facbb4070e0", "82c81327b603430fa5db63d3de2b2572", "e9d171f6d2524cea8c8a82f2d5b93eb1" ] }, "id": "ELdXNMH5YS4y", "outputId": "100153d5-b3ff-4d34-81ac-dda5ac8d8c25" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labels [50258, 50264, 50359, 50363, 10436, 5727, 50257]\n", "Input : 식사\n", "Decode w/ special : <|startoftranscript|><|ko|><|transcribe|><|notimestamps|>식사<|endoftext|>\n", "Decoded w/out special : 식사\n", "Are equal : True\n" ] } ], "source": [ "from transformers import WhisperTokenizer\n", "tokenizer = WhisperTokenizer.from_pretrained(whisper_model , language='ko' , task=\"transcribe\")\n", "\n", "\n", "input_str = dataset[\"train\"][0][\"transcription\"]\n", "labels = tokenizer(input_str).input_ids\n", "decoded_with_special = tokenizer.decode(labels, skip_special_tokens=False)\n", "decoded_str = tokenizer.decode(labels, skip_special_tokens=True)\n", "\n", "print('labels',labels)\n", "print(f\"Input : {input_str}\")\n", "print(f\"Decode w/ special : {decoded_with_special}\")\n", "print(f\"Decoded w/out special : {decoded_str}\")\n", "print(f\"Are equal : {input_str == decoded_str}\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "WgHdV50SkcAg" }, "outputs": [], "source": [ "from datasets import Audio\n", "\n", "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=16000))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gL7fU1gX0C-6", "outputId": "533a8355-165c-4728-98ea-6c325acdc924", "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "torch.cuda.is_available()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "2043f93f4485447ab3781f22727bf2a5", "70c8ffe64518464fbb1731c0b347ea71", "59c08b9a6e83460faa59913614896e76", "0ba00846ee194db8b1a87f5f35bfe8e1", "909c35e3b48e4552bf08864c8b0ad29d", "cb210a814c014c8eaaa07270abc491d0", "afd5b971cd9d4cd68e7e0c623fc861d6", "25e1cd13a3bc42ad958989268d5543ae", "6f04a6429fc74afdb27832c81a28da2d", "50a59f1140a54afc853720a3ad564654", "5d81b089b5e548b4bba9c480f95161d5", "8d1ffaf6081641a49c2e4896d9e814f8", "1c8f8f568a1b4f81a5a97874dc612745", "e56ac8f112ab4d97a014e85f5c44324c", "e63f4730ca0545b187f7696c762ab5d8", "663aaad41b6c402b8eb275007c907c56", "6a0673c497da437783f4c4205cd680a1", "15a267cee8ad4aaba9882e4b9355f277", "7bec7aacb2ac4407a5d28ff8779bcfbb", "88ed4970968b419f8403e3e92e3db1f2", "0be460fb33214642b26478de0de3e369", "bb6590ee6847405d8156eb3de4f95c81" ] }, "id": "HNNBDwVOlLqP", "outputId": "e16699dd-0e7a-4a3c-8286-0523b07a90fb" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e04f7e9ae3454a2bb9eb7e99340293b1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/16548 [00:00 Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lengths and need different padding methods\n", " # first treat the audio inputs by simply returning torch tensors\n", " input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n", " batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n", "\n", "\n", " # get the tokenized label sequences\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", " # pad the labels to max length\n", " labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n", "\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", "\n", " # if bos token is appended in previous tokenization step,\n", " # cut bos token here as it's append later anyways\n", " if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n", " labels = labels[:, 1:]\n", "\n", "\n", " batch[\"labels\"] = labels\n", "\n", "\n", " return batch" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "Q1O2MR3Zm1_3" }, "outputs": [ { "data": { "text/plain": [ "DataCollatorSpeechSeq2SeqWithPadding(processor=WhisperProcessor:\n", "- feature_extractor: WhisperFeatureExtractor {\n", " \"chunk_length\": 30,\n", " \"feature_extractor_type\": \"WhisperFeatureExtractor\",\n", " \"feature_size\": 80,\n", " \"hop_length\": 160,\n", " \"n_fft\": 400,\n", " \"n_samples\": 480000,\n", " \"nb_max_frames\": 3000,\n", " \"padding_side\": \"right\",\n", " \"padding_value\": 0.0,\n", " \"processor_class\": \"WhisperProcessor\",\n", " \"return_attention_mask\": false,\n", " \"sampling_rate\": 16000\n", "}\n", "\n", "- tokenizer: WhisperTokenizer(name_or_path='openai/whisper-small', vocab_size=50258, model_max_length=1024, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|endoftext|>', '<|startoftranscript|>', '<|en|>', '<|zh|>', '<|de|>', '<|es|>', '<|ru|>', '<|ko|>', '<|fr|>', '<|ja|>', '<|pt|>', '<|tr|>', '<|pl|>', '<|ca|>', '<|nl|>', '<|ar|>', '<|sv|>', '<|it|>', '<|id|>', '<|hi|>', '<|fi|>', '<|vi|>', '<|he|>', '<|uk|>', '<|el|>', '<|ms|>', '<|cs|>', '<|ro|>', '<|da|>', '<|hu|>', '<|ta|>', '<|no|>', '<|th|>', '<|ur|>', '<|hr|>', '<|bg|>', '<|lt|>', '<|la|>', '<|mi|>', '<|ml|>', '<|cy|>', '<|sk|>', '<|te|>', '<|fa|>', '<|lv|>', '<|bn|>', '<|sr|>', '<|az|>', '<|sl|>', '<|kn|>', '<|et|>', '<|mk|>', '<|br|>', '<|eu|>', '<|is|>', '<|hy|>', '<|ne|>', '<|mn|>', '<|bs|>', '<|kk|>', '<|sq|>', '<|sw|>', '<|gl|>', '<|mr|>', '<|pa|>', '<|si|>', '<|km|>', '<|sn|>', '<|yo|>', '<|so|>', '<|af|>', '<|oc|>', '<|ka|>', '<|be|>', '<|tg|>', '<|sd|>', '<|gu|>', '<|am|>', '<|yi|>', '<|lo|>', '<|uz|>', '<|fo|>', '<|ht|>', '<|ps|>', '<|tk|>', '<|nn|>', '<|mt|>', '<|sa|>', '<|lb|>', '<|my|>', '<|bo|>', '<|tl|>', '<|mg|>', '<|as|>', '<|tt|>', '<|haw|>', '<|ln|>', '<|ha|>', '<|ba|>', '<|jw|>', '<|su|>', '<|translate|>', '<|transcribe|>', '<|startoflm|>', '<|startofprev|>', '<|nocaptions|>', '<|notimestamps|>']}))" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from transformers import WhisperProcessor\n", "\n", "processor = WhisperProcessor.from_pretrained(whisper_model, language=language, task=\"transcribe\")\n", "\n", "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)\n", "data_collator" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "5_UYluO9m3eg" }, "outputs": [], "source": [ "import evaluate\n", "\n", "metric = evaluate.load(\"wer\")\n", "\n", "def compute_metrics(pred):\n", " pred_ids = pred.predictions\n", " label_ids = pred.label_ids\n", "\n", " # replace -100 with the pad_token_id\n", " label_ids[label_ids == -100] = tokenizer.pad_token_id\n", "\n", " # we do not want to group tokens when computing the metrics\n", " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n", " label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n", "\n", " wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n", " return {\"wer\": wer}" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "FsPt8Qdrm5Ru" }, "outputs": [], "source": [ "from transformers import WhisperForConditionalGeneration\n", "\n", "model = WhisperForConditionalGeneration.from_pretrained(whisper_model)\n", "\n", "model.config.forced_decoder_ids = None\n", "model.config.suppress_tokens = []" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "Q7zic_2Pm7Lg" }, "outputs": [], "source": [ "from transformers import Seq2SeqTrainingArguments\n", "\n", "training_args = Seq2SeqTrainingArguments(\n", " output_dir=model_name, # change to a repo name of your choice\n", " per_device_train_batch_size=8,\n", " gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size\n", " learning_rate=1e-6,\n", " warmup_steps=500,\n", " max_steps=30000,\n", " gradient_checkpointing=True,\n", " fp16=True,\n", " evaluation_strategy=\"steps\",\n", " per_device_eval_batch_size=8,\n", " predict_with_generate=True,\n", " generation_max_length=225,\n", " save_steps=500,\n", " eval_steps=500,\n", " logging_steps=25,\n", " report_to=[\"tensorboard\"],\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"wer\",\n", " greater_is_better=False,\n", " push_to_hub=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import gc\n", "import torch\n", "gc.collect()\n", "torch.cuda.empty_cache()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: git [-v | --version] [-h | --help] [-C ] [-c =]\n", " [--exec-path[=]] [--html-path] [--man-path] [--info-path]\n", " [-p | --paginate | -P | --no-pager] [--no-replace-objects] [--bare]\n", " [--git-dir=] [--work-tree=] [--namespace=]\n", " [--super-prefix=] [--config-env==]\n", " []\n", "\n", "These are common Git commands used in various situations:\n", "\n", "start a working area (see also: git help tutorial)\n", " clone Clone a repository into a new directory\n", " init Create an empty Git repository or reinitialize an existing one\n", "\n", "work on the current change (see also: git help everyday)\n", " add Add file contents to the index\n", " mv Move or rename a file, a directory, or a symlink\n", " restore Restore working tree files\n", " rm Remove files from the working tree and from the index\n", "\n", "examine the history and state (see also: git help revisions)\n", " bisect Use binary search to find the commit that introduced a bug\n", " diff Show changes between commits, commit and working tree, etc\n", " grep Print lines matching a pattern\n", " log Show commit logs\n", " show Show various types of objects\n", " status Show the working tree status\n", "\n", "grow, mark and tweak your common history\n", " branch List, create, or delete branches\n", " commit Record changes to the repository\n", " merge Join two or more development histories together\n", " rebase Reapply commits on top of another base tip\n", " reset Reset current HEAD to the specified state\n", " switch Switch branches\n", " tag Create, list, delete or verify a tag object signed with GPG\n", "\n", "collaborate (see also: git help workflows)\n", " fetch Download objects and refs from another repository\n", " pull Fetch from and integrate with another repository or a local branch\n", " push Update remote refs along with associated objects\n", "\n", "'git help -a' and 'git help -g' list available subcommands and some\n", "concept guides. See 'git help ' or 'git help '\n", "to read about a specific subcommand or concept.\n", "See 'git help git' for an overview of the system.\n" ] } ], "source": [ "!git" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "wM8tDlsJo295", "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Cloning https://huggingface.co/yd97/whisper_fine_tuning_lr_30000 into local empty directory.\n", "C:\\Users\\prlab\\Anaconda3\\envs\\emocog\\lib\\site-packages\\transformers\\optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n", "`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`...\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining LossValidation LossWer
5001.0067001.038567407.799685
10000.5945000.52118543.493691
15000.4458000.41268639.873817
20000.4612000.37652936.253943
25000.3388000.35378932.448738
30000.3281000.33448932.811514
35000.3907000.31702231.589117
40000.4444000.30256532.492114
45000.3174000.28764330.709779
50000.2783000.27682129.436120
55000.3939000.26570129.688486
60000.2841000.25450730.552050
65000.2566000.24598230.291798
70000.2691000.23722030.492902
75000.3047000.22802130.563880
80000.2488000.22017427.933754
85000.1995000.21194926.683754
90000.3350000.20535627.085962
95000.2342000.19760826.462934
100000.1953000.19123725.843849
105000.2144000.18446525.118297
110000.1606000.17935524.223186
115000.2362000.17298423.990536
120000.2273000.16801024.392744
125000.1509000.16364422.708991
130000.1675000.15827021.778391
135000.1961000.15340721.699527
140000.1641000.14793421.017350
145000.1571000.14444821.715300
150000.1810000.14096821.608833
155000.1133000.13686719.601735
160000.1716000.13325919.254732
165000.1418000.12948320.536278
170000.1127000.12636220.571767
175000.1376000.12227519.408517
180000.1155000.11974618.075710
185000.1414000.11614918.462145
190000.1268000.11356918.324132
195000.0937000.11139218.485804
200000.1028000.10861917.511830
205000.1285000.10594218.481861
210000.1069000.10403918.020505
215000.1127000.10228017.653785
220000.1636000.10016517.046530
225000.1007000.09829816.533912
230000.1025000.09646316.944006
235000.0822000.09505316.214511
240000.1231000.09368316.947950
245000.0847000.09211717.038644
250000.0637000.09069016.715300
255000.0678000.08977216.738959
260000.1211000.08889217.097792
265000.0926000.08809117.381703
270000.0641000.08728716.529968
275000.0755000.08673116.455047
280000.1515000.08630416.372240
285000.0698000.08591916.510252
290000.0672000.08545216.309148
295000.0955000.08529916.123817
300000.0656000.08523816.131703

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Several commits (2) will be pushed upstream.\n", "Several commits (3) will be pushed upstream.\n", "Several commits (4) will be pushed upstream.\n", "Several commits (5) will be pushed upstream.\n", "Several commits (6) will be pushed upstream.\n", "Several commits (7) will be pushed upstream.\n", "Several commits (8) will be pushed upstream.\n", "Several commits (2) will be pushed upstream.\n", "Several commits (3) will be pushed upstream.\n", "Several commits (2) will be pushed upstream.\n", "Several commits (3) will be pushed upstream.\n", "Several commits (2) will be pushed upstream.\n", "Several commits (3) will be pushed upstream.\n", "Several commits (4) will be pushed upstream.\n", "Several commits (2) will be pushed upstream.\n", "Several commits (3) will be pushed upstream.\n", "Several commits (4) will be pushed upstream.\n", "Several commits (5) will be pushed upstream.\n", "Several commits (2) will be pushed upstream.\n", "Several commits (3) will be pushed upstream.\n", "Several commits (4) will be pushed upstream.\n", "Several commits (5) will be pushed upstream.\n", "Several commits (6) will be pushed upstream.\n", "Several commits (7) will be pushed upstream.\n", "Several commits (2) will be pushed upstream.\n", "Several commits (3) will be pushed upstream.\n", "Several commits (2) will be pushed upstream.\n", "Several commits (2) will be pushed upstream.\n", "Several commits (3) will be pushed upstream.\n", "Several commits (4) will be pushed upstream.\n", "Several commits (5) will be pushed upstream.\n", "Several commits (2) will be pushed upstream.\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=30000, training_loss=0.234419206905365, metrics={'train_runtime': 135549.5903, 'train_samples_per_second': 1.771, 'train_steps_per_second': 0.221, 'total_flos': 6.924433529438208e+19, 'train_loss': 0.234419206905365, 'epoch': 14.5})" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from transformers import Seq2SeqTrainer\n", "\n", "trainer = Seq2SeqTrainer(\n", " args=training_args,\n", " model=model,\n", " train_dataset=dataset[\"train\"],\n", " eval_dataset=dataset[\"test\"],\n", " data_collator=data_collator,\n", " compute_metrics=compute_metrics,\n", " tokenizer=processor.feature_extractor,\n", ")\n", "\n", "trainer.train()" ] }, { "attachments": { "image.png": { "image/png": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ "![image.png](attachment:image.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "tensorboard --logdir=C:\\Users\\prlab\\Desktop\\emocog\\whisper_fine_tuning_lr\\runs\\Apr11_13-42-45_DESKTOP-62UK6VP" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "WhisperForConditionalGeneration(\n", " (model): WhisperModel(\n", " (encoder): WhisperEncoder(\n", " (conv1): Conv1d(80, 768, kernel_size=(3,), stride=(1,), padding=(1,))\n", " (conv2): Conv1d(768, 768, kernel_size=(3,), stride=(2,), padding=(1,))\n", " (embed_positions): Embedding(1500, 768)\n", " (layers): ModuleList(\n", " (0): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (1): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (2): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (3): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (4): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (5): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (6): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (7): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (8): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (9): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (10): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (11): WhisperEncoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (activation_fn): GELUActivation()\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " )\n", " (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (decoder): WhisperDecoder(\n", " (embed_tokens): Embedding(51865, 768, padding_idx=50257)\n", " (embed_positions): WhisperPositionalEmbedding(448, 768)\n", " (layers): ModuleList(\n", " (0): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (1): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (2): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (3): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (4): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (5): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (6): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (7): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (8): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (9): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (10): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (11): WhisperDecoderLayer(\n", " (self_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (activation_fn): GELUActivation()\n", " (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (encoder_attn): WhisperAttention(\n", " (k_proj): Linear(in_features=768, out_features=768, bias=False)\n", " (v_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (q_proj): Linear(in_features=768, out_features=768, bias=True)\n", " (out_proj): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (fc1): Linear(in_features=768, out_features=3072, bias=True)\n", " (fc2): Linear(in_features=3072, out_features=768, bias=True)\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " )\n", " (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " )\n", " )\n", " (proj_out): Linear(in_features=768, out_features=51865, bias=False)\n", ")" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "p1I_L4zBQjg_", "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'그래도 (FP:뭐) 우리 어머니는'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import json\n", "with open(\"E:/Emocog/자유대화 음성(노인남여)/자유대화 음성(노인남녀)/Validation/[라벨]4.AI스피커/노인남여_노인대화77_F_김XX_62_제주_실내/노인남여_노인대화77_F_김XX_62_제주_실내_84050.json\", 'r',encoding='utf-8') as f:\n", " data = json.load(f)\n", " \n", " # JSON 파일 내용 처리\n", " # 예를 들어, data를 출력해보기\n", "data['발화정보']['stt']" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4bbd7f59e5e54471813a9037c96df78c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading (…)lve/main/config.json: 0%| | 0.00/1.29k [00:00\n" ] }, { "ename": "ValueError", "evalue": "ffmpeg was not found but is required to load audio files from filename", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\transformers\\pipelines\\audio_utils.py:34\u001b[0m, in \u001b[0;36mffmpeg_read\u001b[1;34m(bpayload, sampling_rate)\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mffmpeg_command\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstdin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPIPE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstdout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPIPE\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m ffmpeg_process:\n\u001b[0;32m 35\u001b[0m output_stream \u001b[38;5;241m=\u001b[39m ffmpeg_process\u001b[38;5;241m.\u001b[39mcommunicate(bpayload)\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\subprocess.py:858\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors, text)\u001b[0m\n\u001b[0;32m 855\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mTextIOWrapper(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr,\n\u001b[0;32m 856\u001b[0m encoding\u001b[38;5;241m=\u001b[39mencoding, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m--> 858\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreexec_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclose_fds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43mpass_fds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mstartupinfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreationflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshell\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mp2cread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp2cwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mc2pread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc2pwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[43merrread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 864\u001b[0m \u001b[43m \u001b[49m\u001b[43mrestore_signals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_new_session\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 865\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m 866\u001b[0m \u001b[38;5;66;03m# Cleanup if the child failed starting.\u001b[39;00m\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\subprocess.py:1311\u001b[0m, in \u001b[0;36mPopen._execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session)\u001b[0m\n\u001b[0;32m 1310\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1311\u001b[0m hp, ht, pid, tid \u001b[38;5;241m=\u001b[39m \u001b[43m_winapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCreateProcess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# no special security\u001b[39;49;00m\n\u001b[0;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mclose_fds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreationflags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1316\u001b[0m \u001b[43m \u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1317\u001b[0m \u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1318\u001b[0m \u001b[43m \u001b[49m\u001b[43mstartupinfo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1319\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 1320\u001b[0m \u001b[38;5;66;03m# Child is launched. Close the parent's copy of those pipe\u001b[39;00m\n\u001b[0;32m 1321\u001b[0m \u001b[38;5;66;03m# handles that only the child should have open. You need\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1324\u001b[0m \u001b[38;5;66;03m# pipe will not close when the child process exits and the\u001b[39;00m\n\u001b[0;32m 1325\u001b[0m \u001b[38;5;66;03m# ReadFile will hang.\u001b[39;00m\n", "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] 지정된 파일을 찾을 수 없습니다", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[32], line 13\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m text\n\u001b[0;32m 12\u001b[0m audio \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mE:/Emocog/자유대화 음성(노인남여)/자유대화 음성(노인남녀)/Validation/[원천]4.AI스피커/노인남여_노인대화77_F_김XX_62_제주_실내/노인남여_노인대화77_F_김XX_62_제주_실내_84050.wav\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 13\u001b[0m \u001b[43mtranscribe\u001b[49m\u001b[43m(\u001b[49m\u001b[43maudio\u001b[49m\u001b[43m)\u001b[49m\n", "Cell \u001b[1;32mIn[32], line 9\u001b[0m, in \u001b[0;36mtranscribe\u001b[1;34m(audio)\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtranscribe\u001b[39m(audio):\n\u001b[1;32m----> 9\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[43mpipe\u001b[49m\u001b[43m(\u001b[49m\u001b[43maudio\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m text\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\transformers\\pipelines\\automatic_speech_recognition.py:272\u001b[0m, in \u001b[0;36mAutomaticSpeechRecognitionPipeline.__call__\u001b[1;34m(self, inputs, **kwargs)\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\n\u001b[0;32m 226\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 227\u001b[0m inputs: Union[np\u001b[38;5;241m.\u001b[39mndarray, \u001b[38;5;28mbytes\u001b[39m, \u001b[38;5;28mstr\u001b[39m],\n\u001b[0;32m 228\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 229\u001b[0m ):\n\u001b[0;32m 230\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 231\u001b[0m \u001b[38;5;124;03m Transcribe the audio sequence(s) given as inputs to text. See the [`AutomaticSpeechRecognitionPipeline`]\u001b[39;00m\n\u001b[0;32m 232\u001b[0m \u001b[38;5;124;03m documentation for more information.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[38;5;124;03m `\"\".join(chunk[\"text\"] for chunk in output[\"chunks\"])`.\u001b[39;00m\n\u001b[0;32m 271\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 272\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\transformers\\pipelines\\base.py:1101\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[1;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1099\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterate(inputs, preprocess_params, forward_params, postprocess_params)\n\u001b[0;32m 1100\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ChunkPipeline):\n\u001b[1;32m-> 1101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1102\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1103\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_iterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1104\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreprocess_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpostprocess_params\u001b[49m\n\u001b[0;32m 1105\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1106\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1107\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1108\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1109\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_single(inputs, preprocess_params, forward_params, postprocess_params)\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\transformers\\pipelines\\pt_utils.py:124\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_item()\n\u001b[0;32m 123\u001b[0m \u001b[38;5;66;03m# We're out of items within a batch\u001b[39;00m\n\u001b[1;32m--> 124\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 125\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(item, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n\u001b[0;32m 126\u001b[0m \u001b[38;5;66;03m# We now have a batch of \"inferred things\".\u001b[39;00m\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\transformers\\pipelines\\pt_utils.py:266\u001b[0m, in \u001b[0;36mPipelinePackIterator.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 263\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m accumulator\n\u001b[0;32m 265\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_last:\n\u001b[1;32m--> 266\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n\u001b[0;32m 267\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 268\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed, torch\u001b[38;5;241m.\u001b[39mTensor):\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:628\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 625\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 626\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 627\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 628\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 629\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 631\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:671\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 669\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 670\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 671\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m 672\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m 673\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:34\u001b[0m, in \u001b[0;36m_IterableDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index:\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 34\u001b[0m data\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset_iter\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 35\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[0;32m 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\transformers\\pipelines\\pt_utils.py:183\u001b[0m, in \u001b[0;36mPipelineChunkIterator.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubiterator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(\u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterator), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n\u001b[0;32m 181\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 182\u001b[0m \u001b[38;5;66;03m# Try to return next item\u001b[39;00m\n\u001b[1;32m--> 183\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubiterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 184\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[0;32m 185\u001b[0m \u001b[38;5;66;03m# When a preprocess iterator ends, we can start lookig at the next item\u001b[39;00m\n\u001b[0;32m 186\u001b[0m \u001b[38;5;66;03m# ChunkIterator will keep feeding until ALL elements of iterator\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[38;5;66;03m# Another way to look at it, is we're basically flattening lists of lists\u001b[39;00m\n\u001b[0;32m 190\u001b[0m \u001b[38;5;66;03m# into a single list, but with generators\u001b[39;00m\n\u001b[0;32m 191\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubiterator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(\u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterator), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\transformers\\pipelines\\automatic_speech_recognition.py:327\u001b[0m, in \u001b[0;36mAutomaticSpeechRecognitionPipeline.preprocess\u001b[1;34m(self, inputs, chunk_length_s, stride_length_s, ignore_warning)\u001b[0m\n\u001b[0;32m 324\u001b[0m inputs \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(inputs, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[1;32m--> 327\u001b[0m inputs \u001b[38;5;241m=\u001b[39m \u001b[43mffmpeg_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature_extractor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msampling_rate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 329\u001b[0m stride \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 330\u001b[0m extra \u001b[38;5;241m=\u001b[39m {}\n", "File \u001b[1;32m~\\Anaconda3\\envs\\emocog\\lib\\site-packages\\transformers\\pipelines\\audio_utils.py:37\u001b[0m, in \u001b[0;36mffmpeg_read\u001b[1;34m(bpayload, sampling_rate)\u001b[0m\n\u001b[0;32m 35\u001b[0m output_stream \u001b[38;5;241m=\u001b[39m ffmpeg_process\u001b[38;5;241m.\u001b[39mcommunicate(bpayload)\n\u001b[0;32m 36\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[1;32m---> 37\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mffmpeg was not found but is required to load audio files from filename\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merror\u001b[39;00m\n\u001b[0;32m 38\u001b[0m out_bytes \u001b[38;5;241m=\u001b[39m output_stream[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 39\u001b[0m audio \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfrombuffer(out_bytes, np\u001b[38;5;241m.\u001b[39mfloat32)\n", "\u001b[1;31mValueError\u001b[0m: ffmpeg was not found but is required to load audio files from filename" ] } ], "source": [ "from transformers import pipeline\n", "\n", "\n", "\n", "\n", "pipe = pipeline(\"automatic-speech-recognition\",model=\"yd97/whisper_fine_tuning_lr_30000\",tokenizer=tokenizer)\n", "print(pipe)\n", "def transcribe(audio):\n", " text = pipe(audio)[\"text\"]\n", " return text\n", "\n", "audio = \"E:/Emocog/자유대화 음성(노인남여)/자유대화 음성(노인남녀)/Validation/[원천]4.AI스피커/노인남여_노인대화77_F_김XX_62_제주_실내/노인남여_노인대화77_F_김XX_62_제주_실내_84050.wav\"\n", "transcribe(audio)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "machine_shape": "hm", "provenance": [] }, "gpuClass": "standard", "kernelspec": { "display_name": "emocog", "language": "python", "name": "emocog" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "006dde0de65c4def88a26facbb4070e0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", 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