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],
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"text": [
"Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.48.2)\n",
"Collecting datasets\n",
" Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.17.0)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.28.1)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (1.26.4)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (24.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (6.0.2)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2024.11.6)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers) (2.32.3)\n",
"Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.21.0)\n",
"Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.5.2)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.11/dist-packages (from transformers) (4.67.1)\n",
"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (17.0.0)\n",
"Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
" Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n",
"Collecting xxhash (from datasets)\n",
" Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
"Collecting multiprocess<0.70.17 (from datasets)\n",
" Downloading multiprocess-0.70.16-py311-none-any.whl.metadata (7.2 kB)\n",
"Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
" Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.11)\n",
"Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.4.4)\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.3.2)\n",
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (25.1.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.5.0)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (6.1.0)\n",
"Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (0.2.1)\n",
"Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.18.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (4.12.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.4.1)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2.3.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2025.1.31)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
"Downloading datasets-3.2.0-py3-none-any.whl (480 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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"\u001b[?25hDownloading multiprocess-0.70.16-py311-none-any.whl (143 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
" Attempting uninstall: fsspec\n",
" Found existing installation: fsspec 2024.10.0\n",
" Uninstalling fsspec-2024.10.0:\n",
" Successfully uninstalled fsspec-2024.10.0\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cublas-cu12 12.5.3.2 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-cupti-cu12 12.5.82 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-nvrtc-cu12 12.5.82 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-runtime-cu12 12.5.82 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cudnn-cu12 9.3.0.75 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cufft-cu12 11.2.3.61 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-curand-cu12 10.3.6.82 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusolver-cu12 11.6.3.83 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusparse-cu12 12.5.1.3 which is incompatible.\n",
"torch 2.5.1+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-nvjitlink-cu12 12.5.82 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 184,
"referenced_widgets": [
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]
},
"id": "Jz3daiSAX8Wp",
"outputId": "fba497f9-5abc-4b62-c11e-d79f5ae5f190"
},
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{
"output_type": "display_data",
"data": {
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"README.md: 0%| | 0.00/520 [00:00"
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" \n",
" "
]
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"metadata": {},
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]
},
{
"cell_type": "code",
"source": [
"from transformers import pipeline\n",
"\n",
"pipe = pipeline(\"automatic-speech-recognition\", model=\"facebook/wav2vec2-base-100h\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "blftBn8ZYBjl",
"outputId": "e5c0ba47-8569-45cf-d12a-76995cb896d1"
},
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at facebook/wav2vec2-base-100h were not used when initializing Wav2Vec2ForCTC: ['wav2vec2.mask_time_emb_vector']\n",
"- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-100h and are newly initialized: ['wav2vec2.masked_spec_embed']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
"Device set to use cuda:0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"pipe(sample[\"audio\"].copy())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DbGb7Ohha1jM",
"outputId": "bb60ee50-ef52-40a6-e662-ec324aadaa8a"
},
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'text': 'HE TELLS US THAT AT THIS FESTIVE SEASON OF THE YEAR WITH CHRISTMAUS AND ROSE BEEF LOOMING BEFORE US SIMALYIS DRAWN FROM EATING AND ITS RESULTS OCCUR MOST READILY TO THE MIND'}"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from transformers import pipeline\n",
"\n",
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
"pipe = pipeline(\n",
" \"automatic-speech-recognition\", model=\"openai/whisper-base\", device=device\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RFK-Q0Q-bzvM",
"outputId": "ea86547d-b7bf-4e48-f661-a08bf96b1c3c"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Device set to use cuda:0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"pipe(sample[\"audio\"], max_new_tokens=256)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fTA6PEeob3aY",
"outputId": "39325d42-11b0-439b-e279-d5dd5921e9dd"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/transformers/pipelines/automatic_speech_recognition.py:312: FutureWarning: `max_new_tokens` is deprecated and will be removed in version 4.49 of Transformers. To remove this warning, pass `max_new_tokens` as a key inside `generate_kwargs` instead.\n",
" warnings.warn(\n",
"/usr/local/lib/python3.11/dist-packages/transformers/models/whisper/generation_whisper.py:573: FutureWarning: The input name `inputs` is deprecated. Please make sure to use `input_features` instead.\n",
" warnings.warn(\n",
"Due to a bug fix in https://github.com/huggingface/transformers/pull/28687 transcription using a multilingual Whisper will default to language detection followed by transcription instead of translation to English.This might be a breaking change for your use case. If you want to instead always translate your audio to English, make sure to pass `language='en'`.\n",
"Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.43.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'text': ' He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similarly is drawn from eating and its results occur most readily to the mind.'}"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"source": [
"dataset = load_dataset(\n",
" \"facebook/multilingual_librispeech\", \"spanish\", split=\"test\", streaming=True\n",
")\n",
"sample = next(iter(dataset))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
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},
"id": "VYYOcXqIcuWp",
"outputId": "b6accdec-a693-4086-db8c-54bf153fb77a"
},
"execution_count": 23,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0%| | 0.00/18.1k [00:00"
],
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"source": [
"pipe(sample[\"audio\"].copy(), max_new_tokens=256, generate_kwargs={\"task\": \"transcribe\"})"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GeJDL9vpdM-E",
"outputId": "81ebc443-022f-45c8-ad71-3d68bec24ba0"
},
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"You have passed task=transcribe, but also have set `forced_decoder_ids` to [[1, None], [2, 50359]] which creates a conflict. `forced_decoder_ids` will be ignored in favor of task=transcribe.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'text': ' y las almas buscando alguna livio se revuelven ansiosas y hacen el mundo, que así resulta ser del dolor obra. El dolor o la nada, que entenga corazón venga y escoja.'}"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"source": [
"pipe(sample[\"audio\"], max_new_tokens=256, generate_kwargs={\"task\": \"translate\"})"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "o1rTckaRdUZR",
"outputId": "b10f1286-5bb8-45be-ce4a-dbbbc8ad0cb5"
},
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"You have passed task=translate, but also have set `forced_decoder_ids` to [[1, None], [2, 50359]] which creates a conflict. `forced_decoder_ids` will be ignored in favor of task=translate.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'text': ' and the souls looking for a book, they are relinquished and made in the world, which thus results in the pain of work. The pain or nothing, which in their heart comes and takes.'}"
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"\n",
"target_length_in_m = 5\n",
"\n",
"# convert from minutes to seconds (* 60) to num samples (* sampling rate)\n",
"sampling_rate = pipe.feature_extractor.sampling_rate\n",
"target_length_in_samples = target_length_in_m * 60 * sampling_rate\n",
"\n",
"# iterate over our streaming dataset, concatenating samples until we hit our target\n",
"long_audio = []\n",
"for sample in dataset:\n",
" long_audio.extend(sample[\"audio\"][\"array\"])\n",
" if len(long_audio) > target_length_in_samples:\n",
" break\n",
"\n",
"long_audio = np.asarray(long_audio)\n",
"\n",
"# how did we do?\n",
"seconds = len(long_audio) / 16000\n",
"minutes, seconds = divmod(seconds, 60)\n",
"print(f\"Length of audio sample is {minutes} minutes {seconds:.2f} seconds\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lCpbIrCMdekT",
"outputId": "d1a5d4d7-f4b2-4a36-d2b8-1009b97e54e4"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Length of audio sample is 5.0 minutes 8.85 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"pipe(\n",
" long_audio,\n",
" max_new_tokens=256,\n",
" generate_kwargs={\"task\": \"transcribe\"},\n",
" chunk_length_s=30,\n",
" batch_size=8,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Oz6ny2rbdml3",
"outputId": "f6fcd1a3-d618-4211-c3b8-5887df3f795d"
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'text': ' y las almas buscando alguna livio se revuelven ansiosas y hacen el mundo, que así resulta ser del dolor obra, el dolor o la nada, quien tenga corazón venga y escoja. Cuando calle el dolor, se oye a la muerte las alas tenebrosas batire en los profundos, cual si fuese en las solas del mar de la ilusión, en que los seres en rumbo bogan, donde se mesen, frágiles barquillas, las fugitivas formas, echar fuera la vida y acallar las domésticas cuestiones con el huero fragor de las políticas. No he casado a los hombres que se juntan y gritan. Ojas sus gritos son, que el viento lleva, mientras en silencio su dolor radica. El vivir con el hombre, pobre bestia, te ha dado a casa un anhelaro, ¡curo que el lobo no conoce! Tal vez, cuando costabas la cabeza en mi regazo vagamente soñabas en ser hombre después de muerto. Ser hombre, pobre be. No sabe lo que busca y tú para él mi alma solo tienes esta palabra. Nunca. Adar voces vacías, pobres sillos se juntan y gritan y más gritan y sus penas ocultan y piden no sé qué ni ellos saben, aunque crean sabarlos en su locura. Como todas las penas, aunque tantas, son una sola pena, una sola infinita, soberana, la pena de vivir llevando al todo temblando ante la nada. Pero allí no tendremos en torno de nuestra alma la salma de las cosas de que vive, el alma de los campos, las almas de las rocas, las almas de los árboles y ríos, las de las bestias. Tú has muerto en manse dumbre, tú con dulzura,, entregándote a mí en la supremas omisión de la vida, pero él, el que gime junto a la tumba de su Dios, de su amo, ni morir sabe. Tú, al morir, presentí vagamente vivir en mi memoria, no moríerte del todo. Allá, en el otro mundo, tu alma pobre perro no habrá de recostar en mi regazo espiritual, su espiritual cabeza. La lengua de tu alma pobre, amigo, no la mirá la mano de mi alma. Métete en tu dolor y en él trabaja por escardar la brosa. de Y de este fuerte lace, acaso surja fruto con solador, Será vostro consuelo bañeros en las zondas de las eternas lágrimas que curan, Por fin, toda con goja, Pues en lo eterno del dolor divino, la margura se borra, Y la miseria deja al miserable dulzura muy sabrosa. Si tú no teonas, ¿cómo has de perdonar? Perdónate, perdón, solo perdón, perdón tan solo, solo perdón. Fín del poem. La paz viene del fondo del corazón. Es divin tesoro que en ti dios puso, este soro de amor. Esa inquietud interna que te derrite, es en el infinito que no se extingue, que no se sacia, es porque no perdonas. Ni tú debera leyes, hay un deber tan solo y es el perdón. Perdón es sacrificio del que perdona. Es gracia, donde vino del que el perdón recibe. Es gracia y sacrificio, fruto de amor, de amor, no de justicia, de caridad. Mira, mi pobre amigo, mi fiel creyente, al ver morir tus ojos que me miran, al ver cristalizarse tu mirada, ante fluida, yo también te pregunto, a dónde vamos. Tribunal no levantes dentro de tu alma, mantén la pura, no te juzges en juicio, oye a tu sancia, sancia de paz,'}"
]
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"source": [
"pip install --upgrade evaluate jiwer"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sLq-FHSnqPrZ",
"outputId": "cb9658bf-b127-4fff-f2e3-0435baa57312"
},
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting evaluate\n",
" Downloading evaluate-0.4.3-py3-none-any.whl.metadata (9.2 kB)\n",
"Collecting jiwer\n",
" Downloading jiwer-3.1.0-py3-none-any.whl.metadata (2.6 kB)\n",
"Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from evaluate) (3.2.0)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from evaluate) (1.26.4)\n",
"Requirement already satisfied: dill in /usr/local/lib/python3.11/dist-packages (from evaluate) (0.3.8)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from evaluate) (2.2.2)\n",
"Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.11/dist-packages (from evaluate) (2.32.3)\n",
"Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.11/dist-packages (from evaluate) (4.67.1)\n",
"Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from evaluate) (3.5.0)\n",
"Requirement already satisfied: multiprocess in /usr/local/lib/python3.11/dist-packages (from evaluate) (0.70.16)\n",
"Requirement already satisfied: fsspec>=2021.05.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]>=2021.05.0->evaluate) (2024.9.0)\n",
"Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from evaluate) (0.28.1)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from evaluate) (24.2)\n",
"Requirement already satisfied: click>=8.1.8 in /usr/local/lib/python3.11/dist-packages (from jiwer) (8.1.8)\n",
"Collecting rapidfuzz>=3.9.7 (from jiwer)\n",
" Downloading rapidfuzz-3.12.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from datasets>=2.0.0->evaluate) (3.17.0)\n",
"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets>=2.0.0->evaluate) (17.0.0)\n",
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets>=2.0.0->evaluate) (3.11.11)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from datasets>=2.0.0->evaluate) (6.0.2)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->evaluate) (3.4.1)\n",
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"Downloading evaluate-0.4.3-py3-none-any.whl (84 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading jiwer-3.1.0-py3-none-any.whl (22 kB)\n",
"Downloading rapidfuzz-3.12.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
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"\u001b[?25hInstalling collected packages: rapidfuzz, jiwer, evaluate\n",
"Successfully installed evaluate-0.4.3 jiwer-3.1.0 rapidfuzz-3.12.1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"reference = \"the cat sat on the mat\""
],
"metadata": {
"id": "jN5FPneFqlOR"
},
"execution_count": 34,
"outputs": []
},
{
"cell_type": "code",
"source": [
"prediction = \"the cat sit on the\""
],
"metadata": {
"id": "XHVHgic7qoys"
},
"execution_count": 35,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from evaluate import load\n",
"\n",
"wer_metric = load(\"wer\")\n",
"\n",
"wer = wer_metric.compute(references=[reference], predictions=[prediction])\n",
"\n",
"print(wer)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5czFbM2DoC-Y",
"outputId": "459795f4-a21e-49a6-a74d-f44c15b664c2"
},
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0.3333333333333333\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
"\n",
"normalizer = BasicTextNormalizer()\n",
"\n",
"prediction = \" He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similarly is drawn from eating and its results occur most readily to the mind.\"\n",
"normalized_prediction = normalizer(prediction)\n",
"\n",
"normalized_prediction"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"id": "uQOv07pyoRAk",
"outputId": "7a383557-68ca-41dd-87e2-556b5cad3b64"
},
"execution_count": 38,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"' he tells us that at this festive season of the year with christmas and roast beef looming before us similarly is drawn from eating and its results occur most readily to the mind '"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 38
}
]
},
{
"cell_type": "code",
"source": [
"reference = \"HE TELLS US THAT AT THIS FESTIVE SEASON OF THE YEAR WITH CHRISTMAS AND ROAST BEEF LOOMING BEFORE US SIMILES DRAWN FROM EATING AND ITS RESULTS OCCUR MOST READILY TO THE MIND\"\n",
"normalized_referece = normalizer(reference)\n",
"\n",
"wer = wer_metric.compute(\n",
" references=[normalized_referece], predictions=[normalized_prediction]\n",
")\n",
"wer"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2SbmUp_FoVXN",
"outputId": "3017fdf3-44be-40b2-9a6e-b7b0961d7516"
},
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.0625"
]
},
"metadata": {},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import pipeline\n",
"import torch\n",
"\n",
"if torch.cuda.is_available():\n",
" device = \"cuda:0\"\n",
" torch_dtype = torch.float16\n",
"else:\n",
" device = \"cpu\"\n",
" torch_dtype = torch.float32\n",
"\n",
"pipe = pipeline(\n",
" \"automatic-speech-recognition\",\n",
" model=\"openai/whisper-small\",\n",
" torch_dtype=torch_dtype,\n",
" device=device,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 387,
"referenced_widgets": [
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"15cccc872632474b89aa6b4e84d0214d",
"d2554e1177794596a48f5ca9922ed87e",
"e406024e90074258b6f754db6bbb237a",
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"d3eb563dcc08485eb827972d847f6179",
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"66ea2d14dc844a8aa3b7c95619ac55be",
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"593f61bf0345445395bae1f225f917e1",
"2231ec0c8451486ea58e8fd2b91955ea",
"52c7a8798b8f424d8546d01c856b3036",
"8a0434b70504414aa48b67a88185f002",
"37c89a149608473e82f04c03cb0d26e1",
"12c4a4dec1f4451782fb44a9d84e2801",
"7456e1f4b45342e7bbe69a23f63aed93",
"3a1b8f814ac94d478d72c65568774b6b",
"cfb7e098a3d24ea588340b8c37c9e07b",
"7deea29f747b4334be9886a8d22f1579",
"a5856ab118d74a8980617737b9612721",
"3a7d7d17397c45a9aeecd3bee94599af",
"a736cc3e40444e39ac5fb2b5792a0323",
"036045f891784b60b8b9cbc093c6804b",
"67efdfa0f527475a88063622952b4ced",
"cbc2925bc7bc40d897fa50e6e3f69c47",
"3eeb67fb9c194009a2542a3133e2e67f",
"ba8231f33eb94e7c82f0707337d9c06b",
"13f38c22ef6648b19a6c6db8bfe0b1c5",
"e7c5a9c7a3cb425180631b64e081d004",
"7ccdb5dbc11b4d5c820574058680874e",
"d44d531cca584179879752c5e9907471",
"c01912cbf47e43d0a22ba289bf2c3878",
"47688d143892438184de16699f48f8ba",
"7ef55644c23e4153802a9b175c8dbee3",
"22a7d9b64ff54479aac4bc30f3b21148",
"2560c790cbd44cf58348a12ca7e82785",
"80ce1c8e3ba64d3fb196d8a40da08952",
"eddb1cc17c364c74bc3a5a9ba624cebe",
"3031a7a280cd427a8b0e7369e9d271a5",
"f3922c390c6048d09570c72227b6a785",
"3ec46338bfd74d4490e1c45bf6c3793b",
"42f5bbff5c8b43ee80b4810bd85c5217",
"3fb5194974a74df4a34c7ce6e9abd41c",
"88661e204cdb40118311694d8c967192",
"28360ca2952444b9ba546871026ca983",
"882634da94fa4963965e2d580247f623",
"282ea4ec488844d981b0eadc5253236c",
"76a017639bc7434bba864c210253459c",
"38f4f4f64c6a4923a61e0717ed631963",
"722a503098c343a89e7b2d9ba47728c6",
"6601231a1a9241d79c09051bec92e4e9",
"e65c95ca9f6e4bfaad6f911447bc85cc",
"a2fe6a3b00054dddb6678726e605a6b1",
"8e3877b707754406b6ad165afd8fcfcc",
"0969d76eed6d4185a2a8b4ebcd1df54b",
"8ccae4fab3a1495480691ba9e11d0f8b",
"aacc86bec4a242cbbd540ca454bb96bf",
"1970ca6e13014b418aaba6f857125dff"
]
},
"id": "yiUN5fPGoayU",
"outputId": "79aa05bf-8293-4f90-a973-1a69caff717d"
},
"execution_count": 40,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/1.97k [00:00 0\n",
"]\n",
"all_references_norm = [\n",
" all_references_norm[i]\n",
" for i in range(len(all_references_norm))\n",
" if len(all_references_norm[i]) > 0\n",
"]\n",
"\n",
"wer = 100 * wer_metric.compute(\n",
" references=all_references_norm, predictions=all_predictions_norm\n",
")\n",
"\n",
"wer"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5E-yCIwxvHBT",
"outputId": "0eaeb5d0-888e-45ad-c34a-eaeb63feba01"
},
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"110.52439406057655"
]
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset, DatasetDict\n",
"\n",
"common_voice = DatasetDict()\n",
"\n",
"common_voice[\"train\"] = load_dataset(\n",
" \"mozilla-foundation/common_voice_13_0\", \"dv\", split=\"train+validation\"\n",
")\n",
"common_voice[\"test\"] = load_dataset(\n",
" \"mozilla-foundation/common_voice_13_0\", \"dv\", split=\"test\"\n",
")\n",
"\n",
"print(common_voice)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2ct1FBLsvQOp",
"outputId": "75c4d4f5-9443-430a-fc68-3e85342553b7"
},
"execution_count": 46,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment', 'variant'],\n",
" num_rows: 4904\n",
" })\n",
" test: Dataset({\n",
" features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment', 'variant'],\n",
" num_rows: 2212\n",
" })\n",
"})\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"common_voice = common_voice.select_columns([\"audio\", \"sentence\"])"
],
"metadata": {
"id": "3-kj__bNvTxN"
},
"execution_count": 47,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE\n",
"\n",
"TO_LANGUAGE_CODE"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XzlbEWDwvXUx",
"outputId": "3e9c967c-82f5-4501-fbdb-f9bd81e72a17"
},
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'english': 'en',\n",
" 'chinese': 'zh',\n",
" 'german': 'de',\n",
" 'spanish': 'es',\n",
" 'russian': 'ru',\n",
" 'korean': 'ko',\n",
" 'french': 'fr',\n",
" 'japanese': 'ja',\n",
" 'portuguese': 'pt',\n",
" 'turkish': 'tr',\n",
" 'polish': 'pl',\n",
" 'catalan': 'ca',\n",
" 'dutch': 'nl',\n",
" 'arabic': 'ar',\n",
" 'swedish': 'sv',\n",
" 'italian': 'it',\n",
" 'indonesian': 'id',\n",
" 'hindi': 'hi',\n",
" 'finnish': 'fi',\n",
" 'vietnamese': 'vi',\n",
" 'hebrew': 'he',\n",
" 'ukrainian': 'uk',\n",
" 'greek': 'el',\n",
" 'malay': 'ms',\n",
" 'czech': 'cs',\n",
" 'romanian': 'ro',\n",
" 'danish': 'da',\n",
" 'hungarian': 'hu',\n",
" 'tamil': 'ta',\n",
" 'norwegian': 'no',\n",
" 'thai': 'th',\n",
" 'urdu': 'ur',\n",
" 'croatian': 'hr',\n",
" 'bulgarian': 'bg',\n",
" 'lithuanian': 'lt',\n",
" 'latin': 'la',\n",
" 'maori': 'mi',\n",
" 'malayalam': 'ml',\n",
" 'welsh': 'cy',\n",
" 'slovak': 'sk',\n",
" 'telugu': 'te',\n",
" 'persian': 'fa',\n",
" 'latvian': 'lv',\n",
" 'bengali': 'bn',\n",
" 'serbian': 'sr',\n",
" 'azerbaijani': 'az',\n",
" 'slovenian': 'sl',\n",
" 'kannada': 'kn',\n",
" 'estonian': 'et',\n",
" 'macedonian': 'mk',\n",
" 'breton': 'br',\n",
" 'basque': 'eu',\n",
" 'icelandic': 'is',\n",
" 'armenian': 'hy',\n",
" 'nepali': 'ne',\n",
" 'mongolian': 'mn',\n",
" 'bosnian': 'bs',\n",
" 'kazakh': 'kk',\n",
" 'albanian': 'sq',\n",
" 'swahili': 'sw',\n",
" 'galician': 'gl',\n",
" 'marathi': 'mr',\n",
" 'punjabi': 'pa',\n",
" 'sinhala': 'si',\n",
" 'khmer': 'km',\n",
" 'shona': 'sn',\n",
" 'yoruba': 'yo',\n",
" 'somali': 'so',\n",
" 'afrikaans': 'af',\n",
" 'occitan': 'oc',\n",
" 'georgian': 'ka',\n",
" 'belarusian': 'be',\n",
" 'tajik': 'tg',\n",
" 'sindhi': 'sd',\n",
" 'gujarati': 'gu',\n",
" 'amharic': 'am',\n",
" 'yiddish': 'yi',\n",
" 'lao': 'lo',\n",
" 'uzbek': 'uz',\n",
" 'faroese': 'fo',\n",
" 'haitian creole': 'ht',\n",
" 'pashto': 'ps',\n",
" 'turkmen': 'tk',\n",
" 'nynorsk': 'nn',\n",
" 'maltese': 'mt',\n",
" 'sanskrit': 'sa',\n",
" 'luxembourgish': 'lb',\n",
" 'myanmar': 'my',\n",
" 'tibetan': 'bo',\n",
" 'tagalog': 'tl',\n",
" 'malagasy': 'mg',\n",
" 'assamese': 'as',\n",
" 'tatar': 'tt',\n",
" 'hawaiian': 'haw',\n",
" 'lingala': 'ln',\n",
" 'hausa': 'ha',\n",
" 'bashkir': 'ba',\n",
" 'javanese': 'jw',\n",
" 'sundanese': 'su',\n",
" 'cantonese': 'yue',\n",
" 'burmese': 'my',\n",
" 'valencian': 'ca',\n",
" 'flemish': 'nl',\n",
" 'haitian': 'ht',\n",
" 'letzeburgesch': 'lb',\n",
" 'pushto': 'ps',\n",
" 'panjabi': 'pa',\n",
" 'moldavian': 'ro',\n",
" 'moldovan': 'ro',\n",
" 'sinhalese': 'si',\n",
" 'castilian': 'es',\n",
" 'mandarin': 'zh'}"
]
},
"metadata": {},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import WhisperProcessor\n",
"\n",
"processor = WhisperProcessor.from_pretrained(\n",
" \"openai/whisper-small\", language=\"sinhalese\", task=\"transcribe\"\n",
")"
],
"metadata": {
"id": "L2HvHW5NvcQc"
},
"execution_count": 49,
"outputs": []
},
{
"cell_type": "code",
"source": [
"common_voice[\"train\"].features"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h0L5xepHvg73",
"outputId": "f9a15c1d-2d98-484a-a907-6c43061f1d8e"
},
"execution_count": 50,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'audio': Audio(sampling_rate=48000, mono=True, decode=True, id=None),\n",
" 'sentence': Value(dtype='string', id=None)}"
]
},
"metadata": {},
"execution_count": 50
}
]
},
{
"cell_type": "code",
"source": [
"from datasets import Audio\n",
"\n",
"sampling_rate = processor.feature_extractor.sampling_rate\n",
"common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 375
},
"id": "mzjv2RfOvkob",
"outputId": "e67e8ae5-b317-4c60-faf6-24511620d75b"
},
"execution_count": 13,
"outputs": [
{
"output_type": "error",
"ename": "ModuleNotFoundError",
"evalue": "No module named 'datasets'",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAudio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0msampling_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_extractor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampling_rate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mcommon_voice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommon_voice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"audio\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAudio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampling_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msampling_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'datasets'",
"",
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
],
"errorDetails": {
"actions": [
{
"action": "open_url",
"actionText": "Open Examples",
"url": "/notebooks/snippets/importing_libraries.ipynb"
}
]
}
}
]
},
{
"cell_type": "code",
"source": [
"def prepare_dataset(example):\n",
" audio = example[\"audio\"]\n",
"\n",
" example = processor(\n",
" audio=audio[\"array\"],\n",
" sampling_rate=audio[\"sampling_rate\"],\n",
" text=example[\"sentence\"],\n",
" )\n",
"\n",
" # compute input length of audio sample in seconds\n",
" example[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
"\n",
" return example"
],
"metadata": {
"id": "sdCKGsu5vonz"
},
"execution_count": 52,
"outputs": []
},
{
"cell_type": "code",
"source": [
"common_voice = common_voice.map(\n",
" prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=1\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"b4b2d4b14f044e39b2815d73f47c350b",
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"e16229c5ddc6437e811935169e3ea523",
"74b27da6ea8e46e590c8a41e8fcea52f",
"392fdd4c996d4eaea4acc01b1ff86c50",
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"1c2edfed6ee6436880f99e0b53b7e71f",
"0ee0ed58c2f14382992bea239e1ee7db",
"b9a5906e5af240fe91ca6a8660fe6f70",
"b4928b9809a3436e8d1de773e0f92f7d",
"fecbb5c38926443ba52ad209bc3018e7",
"c022629d01b940d0a0304c14444d95c7",
"cadb8c6dceae4f548948904dd7208b7d",
"00c0af72037444eca0c52d94ff54a2fb",
"8e183e9ab5154fc282c959d214bc5ad6",
"d7da2bb68eab4072971a6437978bfd21",
"8e4416c32d1a489ca989e2eeb82aa2b0",
"5fdd1a0528d6492089ba31d84cf9526c",
"b0f290a7f0f448ad83ca99c80715ae2a",
"db32f3c32a264c1283ed8331ca2ee1af"
]
},
"id": "K5J1rylmvr-9",
"outputId": "2b6bb7e2-2a37-41a6-e438-360d8ffccf7a"
},
"execution_count": 53,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/4904 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcommon_voice\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'common_voice' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "rnFjX3NCzMUW"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import torch\n",
"\n",
"from dataclasses import dataclass\n",
"from typing import Any, Dict, List, Union\n",
"\n",
"\n",
"@dataclass\n",
"class DataCollatorSpeechSeq2SeqWithPadding:\n",
" processor: Any\n",
"\n",
" def __call__(\n",
" self, features: List[Dict[str, Union[List[int], torch.Tensor]]]\n",
" ) -> 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 = [\n",
" {\"input_features\": feature[\"input_features\"][0]} for feature in features\n",
" ]\n",
" batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\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",
" # replace padding with -100 to ignore loss correctly\n",
" labels = labels_batch[\"input_ids\"].masked_fill(\n",
" 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",
" batch[\"labels\"] = labels\n",
"\n",
" return batch"
],
"metadata": {
"id": "yL-ycmm4zP1G"
},
"execution_count": 57,
"outputs": []
},
{
"cell_type": "code",
"source": [
"data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
],
"metadata": {
"id": "JJdWcCt9zT5A"
},
"execution_count": 58,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import evaluate\n",
"\n",
"metric = evaluate.load(\"wer\")"
],
"metadata": {
"id": "6igudTYRzXQA"
},
"execution_count": 59,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
"\n",
"normalizer = BasicTextNormalizer()\n",
"\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] = processor.tokenizer.pad_token_id\n",
"\n",
" # we do not want to group tokens when computing the metrics\n",
" pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
" label_str = processor.batch_decode(label_ids, skip_special_tokens=True)\n",
"\n",
" # compute orthographic wer\n",
" wer_ortho = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
"\n",
" # compute normalised WER\n",
" pred_str_norm = [normalizer(pred) for pred in pred_str]\n",
" label_str_norm = [normalizer(label) for label in label_str]\n",
" # filtering step to only evaluate the samples that correspond to non-zero references:\n",
" pred_str_norm = [\n",
" pred_str_norm[i] for i in range(len(pred_str_norm)) if len(label_str_norm[i]) > 0\n",
" ]\n",
" label_str_norm = [\n",
" label_str_norm[i]\n",
" for i in range(len(label_str_norm))\n",
" if len(label_str_norm[i]) > 0\n",
" ]\n",
"\n",
" wer = 100 * metric.compute(predictions=pred_str_norm, references=label_str_norm)\n",
"\n",
" return {\"wer_ortho\": wer_ortho, \"wer\": wer}"
],
"metadata": {
"id": "QtjT8g_GzacQ"
},
"execution_count": 60,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers import WhisperForConditionalGeneration\n",
"\n",
"model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-small\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 240,
"referenced_widgets": [
"58806bc9d3174955985f02ad10cc4600",
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"f87c4cab4c0c46b480c5f03acb54b7cc",
"98c989d8d62b4ffe9ea8de1de5341c8b",
"d156d3721d624313a93b655dc8fb8eb2",
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"a3c0d1dd438647b2aacdb67792186a96",
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"ef1c419ad80143f690876234b7ba764c",
"d23ca59674a2420dbbe4d0a9ae7c64a1",
"ac4281b14efa49e0acb2f6d52cfbf988",
"8716ee20f4c041779380d5484b21c1da",
"03d7d15c134c426baf7dc3120a5d046a",
"06c2304b70bd4f5f9b1746067c825a06",
"b10220a2f5e14c56a81e660f784a9689",
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"82f079fe71c84168860ca8dd54203cb3",
"cfa92ec4560a4d399a8887bdd4d7c1c3",
"716d8d9fd3c44c8ba4ee39c80da8bfbc",
"328f8932f8de4e7a8cf0f8ab5d9b12b8",
"2842cc33dfb049f9bbfd79acaf332caf",
"e3112c1053854e1a976ed0c833550179",
"bf358fa3beec4a969087791d8e59dc26",
"9082186be73046a2aeb6e697aa7d9519"
]
},
"id": "b28hbgkpzew_",
"outputId": "266572d5-ba73-4c78-9a08-bf4c41825f84"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/1.97k [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcommon_voice\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0meval_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcommon_voice\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"test\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mdata_collator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_collator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'common_voice' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 144
},
"id": "REG3WDHnztfy",
"outputId": "13aa0879-e58d-4a5a-b9b0-2c00cd35a6fd"
},
"execution_count": 3,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 'trainer' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'trainer' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [
"kwargs = {\n",
" \"dataset_tags\": \"mozilla-foundation/common_voice_13_0\",\n",
" \"dataset\": \"Common Voice 13\", # a 'pretty' name for the training dataset\n",
" \"language\": \"dv\",\n",
" \"model_name\": \"Whisper Small Dv - Sanchit Gandhi\", # a 'pretty' name for your model\n",
" \"finetuned_from\": \"openai/whisper-small\",\n",
" \"tasks\": \"automatic-speech-recognition\",\n",
"}"
],
"metadata": {
"id": "f52KYjjLDged"
},
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"source": [
"trainer.push_to_hub(**kwargs)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 144
},
"id": "0EbmS8_8Dj_r",
"outputId": "7b0cbc48-0c75-4044-b463-670f3de3dc01"
},
"execution_count": 2,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 'trainer' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m| \u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'trainer' is not defined"
]
}
]
}
]
} | | | | |