{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "LBSYoWbi-45k" }, "source": [ "# **Fine-tuning Speech Model with 🤗 Transformers**" ] }, { "cell_type": "markdown", "metadata": { "id": "fWj4gag6uH2I" }, "source": [ "This notebook shows how to fine-tune multi-lingual pretrained speech models for Automatic Speech Recognition." ] }, { "cell_type": "markdown", "metadata": { "id": "MnXLL0QhuMBD" }, "source": [ "This notebook is built to run on the [TIMIT dataset](https://huggingface.co/datasets/timit) with any speech model checkpoint from the [Model Hub](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads) as long as that model has a version with a Connectionist Temporal Classification (CTC) head. Depending on the model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those two parameters, then the rest of the notebook should run smoothly:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "v7CftmzCuKYl" }, "outputs": [], "source": [ "model_checkpoint = \"facebook/wav2vec2-base\"\n", "batch_size = 32" ] }, { "cell_type": "markdown", "metadata": { "id": "HTSa1gvYuUX0" }, "source": [ "For a more in-detail explanation of how multi-lingual pretrained speech models function, please take a look at the [🤗 Blog](https://huggingface.co/blog/fine-tune-wav2vec2-english)." ] }, { "cell_type": "markdown", "metadata": { "id": "e335hPmdtASZ" }, "source": [ "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `librosa` package to load audio files and the `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "c8eh87Hoee5d" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets==1.14\n", "!pip install transformers==4.11.3\n", "!pip install librosa\n", "!pip install jiwer" ] }, { "cell_type": "markdown", "metadata": { "id": "8_6kYmDMH9lR" }, "source": [ "Next we strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training. \n", "\n", "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 239, "referenced_widgets": [ "1e0fc323b5744fa4a9f52e19ed17e51f", "e009bda61c6844d7a195937cd3edc69d", "e8efe81123244cdc9d923f213a28cc0f", "bdbf48017d7b46aa9175d60413d596ba", "f6f802e961ef4be6a7c3df9edd382497", "aa560b9c74c648968dc9e06d3f779b1b", "68742324ad074322b18d26db0a6887e6", "eaf82b66a8dc4024823253ee260f79b5", "20aa14cdf93848969cb5fc746c12f476", "1a6890f25d1346b9ac9c503f07d3c415", "de28bc73b90b4b7d854106dcc8d315f3", "9d15a9f1b03546fea5fbca5e39e9daaa", "c3fa5eee9e544fc2a8faf9eef90d4131", "966a899fc3d345329c577a10234892df", "e09c7201febc4d58bce738e843ba61b8", "4d01d067330840b1927ab1da60d1e4bb" ] }, "id": "zFLBDyzQIA3R", "outputId": "ca186839-e9ec-4013-a9ec-2a10d4ec117b" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e0fc323b5744fa4a9f52e19ed17e51f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"
\\n\n", " \n", " \n", " \n", " text\n", " \n", " \n", " \n", " \n", " 0\n", " Help celebrate your brother's success.\n", " \n", " \n", " 1\n", " His black hat with its wide brim, high crown, and fur trim rode high.\n", " \n", " \n", " 2\n", " Remember to allow identical twins to enter freely.\n", " \n", " \n", " 3\n", " We experience distress and frustration obtaining our degrees.\n", " \n", " \n", " 4\n", " First add milk to the shredded cheese.\n", " \n", " \n", " 5\n", " Tiny bodies, dropped onto a dry leaf, made a pile as big as a small apple.\n", " \n", " \n", " 6\n", " But it must be remembered that the plan should not be oriented geographically.\n", " \n", " \n", " 7\n", " How would you evaluate this algebraic expression?\n", " \n", " \n", " 8\n", " Academic aptitude guarantees your diploma.\n", " \n", " \n", " 9\n", " Serve in frankfurter buns or as a meat dish.\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_random_elements(timit[\"train\"].remove_columns([\"audio\", \"file\"]), num_examples=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "fowcOllGNNju" }, "source": [ "Alright! The transcriptions look very clean and the language seems to correspond more to written text than dialogue. This makes sense taking into account that [Timit](https://huggingface.co/datasets/timit_asr) is a read speech corpus." ] }, { "cell_type": "markdown", "metadata": { "id": "vq7OR50LN49m" }, "source": [ "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n", "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n", "\n", "In addition, we normalize the text to only have lower case letters and append a word separator token at the end." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "svKzVJ_hQGK6" }, "outputs": [], "source": [ "import re\n", "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"]'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"text\"] = re.sub(chars_to_ignore_regex, '', batch[\"text\"]).lower() + \" \"\n", " return batch" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "30f8754cdd4d42d18af21e4db16b615c", "ebd5afd701624f289204d48638ed2fbc", "c2c33b1d592b4b06a7b7e6f6056abae0", "af35e4473888416a9419c65fe7e56859", "bfb469bf394a4aa288444a43e55ca707", "01ae96853d54497f9108870821c45470", "f2a98ab2360c418cb7c7d5a71b3d1361", "355b32da63cc40ce9c644a3986f1340d", "b6fa9dd697cd47f59ba0af7602feabbe", "de9fd72fa9444ac4bc6f88242f20126b", "05c060b08e2e4040ae380608a4642167", "46efc2b990144d689404717608fc7d7b", "978effc6f8a34fbf9b2f2b407a1f005a", "905011f308344ad88703b8169d3378fa", "d2a85d56e6f6498ab331d5f893a80f9a", "87a281d8e1104b5ab9c705d28804692f", "22b4067dd7774910b6ccd0a422e9aa02", "ac812fac6aca4242b8b43f61e251be1c", "ee3b7afab0f349b2a8a7a2c0e157f851", "2559c18dea0a428697b916de32e1bc0d", "b3871236afda4d6a84413d1b8e833c38", "d30cf357ba084b15a0ab7c785cd632f5" ] }, "id": "XIHocAuTQbBR", "outputId": "219d5e95-d26e-491b-c2a4-5120eb131086" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "30f8754cdd4d42d18af21e4db16b615c", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4620 [00:00\n", " \n", " \n", " \n", " text\n", " \n", " \n", " \n", " \n", " 0\n", " don't ask me to carry an oily rag like that\n", " \n", " \n", " 1\n", " porcupines resemble sea urchins\n", " \n", " \n", " 2\n", " challenge each general's intelligence\n", " \n", " \n", " 3\n", " like enough we'll all be up on top by sundown\n", " \n", " \n", " 4\n", " the courier was a dwarf\n", " \n", " \n", " 5\n", " civilization is what man has made of himself\n", " \n", " \n", " 6\n", " may i order a strawberry sundae after i eat dinner\n", " \n", " \n", " 7\n", " she had your dark suit in greasy wash water all year\n", " \n", " \n", " 8\n", " my ideal morning begins with hot coffee\n", " \n", " \n", " 9\n", " she uses both names interchangeably\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_random_elements(timit[\"train\"].remove_columns([\"audio\", \"file\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "jwfaptH5RJwA" }, "source": [ "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n", "\n", "In CTC, it is common to classify speech chunks into letters, so we will do the same here. \n", "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n", "\n", "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. \n", "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "LwCshNbbeRZR" }, "outputs": [], "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"text\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "f67c747217894a0fb1ab34baadf0599f", "94435d4e63bd4d96acc8cf2cd5a13972", "eae6bab3eaf144a09787d03b0ca098d6", "456331da10564aa38300266f8fbbb51d", "7292b33c3cba4b17b1244304d3ce3085", "e4c489c42f2741b8afbef75d3a434084", "fdd3ffacd3f34c66964b6f85402540fd", "e85f07ee767c4f1e89b9b58c528e9502", "5c72edf8f3624a4c80a0724280514c7c", "f7dc013178f5427aab2a5caf9ce36dbf", "cf93cf23940847c3b402c367f49d2aad", "0ccb540f2e4e4366afbf6f3c7b6a1891", "8bd3a4b8a3954fcdba93dd67ff9a6461", "61e6f51635824da78940b6cd88a74a51", "de7353a43be34e5f9251f8173f82b5d7", "b3fa310071864a698c6af603130cabaf", "d717063846d849b6aa383e50f5c93aea", "96700aa06f35440384cf99ee2cb3aeaa", "7940a72efa9b47ba9ed97668ed090bcf", "9522fbb829c940d78c6ad7dae3faf8b1", "32606f13dfd9459db69cfa912949629a", "76f870c8d7d7400fa9fecf4dc46212dc" ] }, "id": "_m6uUjjcfbjH", "outputId": "c08d0157-9d33-4938-e56d-3bbe1c08093d" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f67c747217894a0fb1ab34baadf0599f", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1 [00:00 main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-colab/commit/870c48e622b77a3b27d61c094165c51d4aef9283'" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.push_to_hub(repo_name)" ] }, { "cell_type": "markdown", "metadata": { "id": "KvL12DrNV4cx" }, "source": [ "Great, you can see the just created repository under `https://huggingface.co//wav2vec2-base-timit-demo-colab`" ] }, { "cell_type": "markdown", "metadata": { "id": "YFmShnl7RE35" }, "source": [ "### Preprocess Data\n", "\n", "So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to `'text'`, our datasets include two more column names `'file'` and `'audio'`. `'file'` states the absolute path of the audio file. Let's take a look." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "TTCS7W6XJ9BG", "outputId": "5d936122-6479-4f3c-84d8-896dfcebcd3c" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timit[\"train\"][0][\"file\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "BwxprOw4Nzrl" }, "source": [ "`Wav2Vec2` expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n", "\n", " Thankfully, `datasets` does this automatically when calling the column `audio`. Let try it out. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mk9QHuSsN7lf", "outputId": "c49d2f4d-6e9a-489e-a419-772954f80bf1" }, "outputs": [ { "data": { "text/plain": [ "{'array': array([-2.1362305e-04, 6.1035156e-05, 3.0517578e-05, ...,\n", " -3.0517578e-05, -9.1552734e-05, -6.1035156e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV',\n", " 'sampling_rate': 16000}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timit[\"train\"][0][\"audio\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "wSBIGEiaKHMn" }, "source": [ "We can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 1.13.3`, which loads and resamples audio files on-the-fly upon calling.\n", "\n", "The sampling rate is set to 16kHz which is what `Wav2Vec2` expects as an input." ] }, { "cell_type": "markdown", "metadata": { "id": "SOckzFd4Mbzq" }, "source": [ "Great, let's listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded. \n", "\n", "**Note**: *You can click the following cell a couple of times to listen to different speech samples.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 92 }, "id": "dueM6U7Ev0OA", "outputId": "17d78b9b-a940-47e5-f546-87fac22b036e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the triumphant warrior exhibited naive heroism \n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import IPython.display as ipd\n", "import numpy as np\n", "import random\n", "\n", "rand_int = random.randint(0, len(timit[\"train\"]))\n", "\n", "print(timit[\"train\"][rand_int][\"text\"])\n", "ipd.Audio(data=np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]), autoplay=True, rate=16000)" ] }, { "cell_type": "markdown", "metadata": { "id": "1MaL9J2dNVtG" }, "source": [ "It can be heard, that the speakers change along with their speaking rate, accent, etc. Overall, the recordings sound relatively clear though, which is to be expected from a read speech corpus.\n", "\n", "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n", "\n", "**Note**: *You can click the following cell a couple of times to verify multiple samples.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "3b0417f4-9e15-44d0-f95f-2de657016c41" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target text: she had your dark suit in greasy wash water all year \n", "Input array shape: (57242,)\n", "Sampling rate: 16000\n" ] } ], "source": [ "rand_int = random.randint(0, len(timit[\"train\"]))\n", "\n", "print(\"Target text:\", timit[\"train\"][rand_int][\"text\"])\n", "print(\"Input array shape:\", np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]).shape)\n", "print(\"Sampling rate:\", timit[\"train\"][rand_int][\"audio\"][\"sampling_rate\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "M9teZcSwOBJ4" }, "source": [ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized.\n", "\n", "Next, we should process the data with the model's feature extractor. Let's load the feature extractor" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FFQCC3A9vz2r" }, "outputs": [], "source": [ "from transformers import AutoFeatureExtractor\n", "\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)" ] }, { "cell_type": "markdown", "metadata": { "id": "BpJDkK8Pv4cM" }, "source": [ "and wrap it into a Wav2Vec2Processor together with the tokenizer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "d-EuXD_rv7FP" }, "outputs": [], "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ] }, { "cell_type": "markdown", "metadata": { "id": "HfD3cVnSv9Tp" }, "source": [ "Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by the model for training. To do so let's make use of Dataset's [`map(...)`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=map#datasets.DatasetDict.map) function.\n", "\n", "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n", "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). \n", "Third, we encode the transcriptions to label ids." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eJY7I0XAwe9p" }, "outputs": [], "source": [ "def prepare_dataset(batch):\n", " audio = batch[\"audio\"]\n", "\n", " # batched output is \"un-batched\" to ensure mapping is correct\n", " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n", " batch[\"input_length\"] = len(batch[\"input_values\"])\n", " \n", " with processor.as_target_processor():\n", " batch[\"labels\"] = processor(batch[\"text\"]).input_ids\n", " return batch" ] }, { "cell_type": "markdown", "metadata": { "id": "hVMZhH4-nP8-" }, "source": [ "Let's apply the data preparation function to all examples." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-np9xYK-wl8q" }, "outputs": [], "source": [ "timit = timit.map(prepare_dataset, remove_columns=timit.column_names[\"train\"], num_proc=4)" ] }, { "cell_type": "markdown", "metadata": { "id": "p_MuJSH8nTuQ" }, "source": [ "**Note**: Currently `datasets` make use of [`torchaudio`](https://pytorch.org/audio/stable/index.html) and [`librosa`](https://librosa.org/doc/latest/index.html) for audio loading and resampling. If you wish to implement your own costumized data loading/sampling, feel free to just make use of the `\"path\"` column instead and disregard the `\"audio\"` column." ] }, { "cell_type": "markdown", "metadata": { "id": "M4J0bU1WsvAg" }, "source": [ "Long input sequences require a lot of memory. Since `Wav2Vec2` is based on `self-attention` the memory requirement scales quadratically with the input length for long input sequences (*cf.* with [this](https://www.reddit.com/r/MachineLearning/comments/genjvb/d_why_is_the_maximum_input_sequence_length_of/) reddit post). For this demo, let's filter all sequences that are longer than 4 seconds out of the training dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "d896231bf5824211a55d494d48626fc6", "14dae264619940beab57908b986aa6bb", "2392ea8eae7c4dbebc0e218cd7c5ce23", "effd4c06dcdb44ae9027dbe846438c83", "3d0635b79b6342b78a9689e092f60872", "2d77f792dd6048b39d0f14ebf4f80614", "d16ea3ef10684bf1b1092345ca92897a", "d7a01acdb98f435489fed6bf1a5b3e4f", "9ceca09d9fdb4a0495d33bf3eef7e65e", "29105b8924d949b99cc0bf317d63b25c", "d1693414e7964bfcbe95f6d95d5d4204" ] }, "id": "nqGobEPUvG3v", "outputId": "3be8c879-ed75-468b-b86a-50b2d64607a9" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d896231bf5824211a55d494d48626fc6", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/5 [00:00=\n", " 7.5 (Volta).\n", " \"\"\"\n", "\n", " processor: Wav2Vec2Processor\n", " padding: Union[bool, str] = True\n", " max_length: Optional[int] = None\n", " max_length_labels: Optional[int] = None\n", " pad_to_multiple_of: Optional[int] = None\n", " pad_to_multiple_of_labels: Optional[int] = None\n", "\n", " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " max_length=self.max_length,\n", " pad_to_multiple_of=self.pad_to_multiple_of,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " max_length=self.max_length_labels,\n", " pad_to_multiple_of=self.pad_to_multiple_of_labels,\n", " 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", " batch[\"labels\"] = labels\n", "\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lbQf5GuZyQ4_" }, "outputs": [], "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "xO-Zdj-5cxXp" }, "source": [ "Next, the evaluation metric is defined. As mentioned earlier, the \n", "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "fd43a043ce7c4b878ade15f6bbf55977", "b362a9179f894ff5b879bbae73fe41b2", "e0888123f0d2455d9f565939a8abda4e", "3544f17e723f4ade9356e0eacd57c4f5", "df32789a34fe494c9a8791a8d8c70c69", "2e27a56468684597ba7084d662a92309", "57cd1596aead47ad85987147a0f1bb75", "4ab4afbba4a940cba487688b298092cf", "a5858da3495c4995adf804fc96ef938f", "a6e7f6bb9bf5486389689d79e499869b", "b5f579a82ca745e1b8baeff5df14e6b3" ] }, "id": "9Xsux2gmyXso", "outputId": "d05825d4-1e5e-47c4-e494-a28e33a20534" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fd43a043ce7c4b878ade15f6bbf55977", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.95k [00:00> m$.\n", "\n", "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1XZ-kjweyTy_" }, "outputs": [], "source": [ "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_ids = np.argmax(pred_logits, axis=-1)\n", "\n", " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n", "\n", " pred_str = processor.batch_decode(pred_ids)\n", " # we do not want to group tokens when computing the metrics\n", " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n", "\n", " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ] }, { "cell_type": "markdown", "metadata": { "id": "Xmgrx4bRwLIH" }, "source": [ "Now, we can load the pretrained `Wav2Vec2` checkpoint. The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of a CTC speech model also CTC's *blank token* ${}^2$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e7cqAWIayn6w", "outputId": "937bae2c-2922-4f76-80f1-1986a4084cec" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "loading configuration file https://huggingface.co/facebook/wav2vec2-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/c7746642f045322fd01afa31271dd490e677ea11999e68660a92619ec7c892b4.02212753c42f07ecd65bbe35175ac4866badb735f9dae5bf2ae455c57db4dbb7\n", "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py:337: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n", " \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\n", "Model config Wav2Vec2Config {\n", " \"activation_dropout\": 0.0,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 256,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": false,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"mean\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": false,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_norm\": \"group\",\n", " \"feat_proj_dropout\": 0.1,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"freeze_feat_extract_train\": true,\n", " \"gradient_checkpointing\": true,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.05,\n", " \"mask_channel_length\": 10,\n", " \"mask_channel_min_space\": 1,\n", " \"mask_channel_other\": 0.0,\n", " \"mask_channel_prob\": 0.0,\n", " \"mask_channel_selection\": \"static\",\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_space\": 1,\n", " \"mask_time_other\": 0.0,\n", " \"mask_time_prob\": 0.05,\n", " \"mask_time_selection\": \"static\",\n", " \"model_type\": \"wav2vec2\",\n", " \"no_mask_channel_overlap\": false,\n", " \"no_mask_time_overlap\": false,\n", " \"num_attention_heads\": 12,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 12,\n", " \"num_negatives\": 100,\n", " \"pad_token_id\": 29,\n", " \"proj_codevector_dim\": 256,\n", " \"transformers_version\": \"4.11.3\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 32\n", "}\n", "\n", "loading weights file https://huggingface.co/facebook/wav2vec2-base/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/ef45231897ce572a660ebc5a63d3702f1a6041c4c5fb78cbec330708531939b3.fcae05302a685f7904c551c8ea571e8bc2a2c4a1777ea81ad66e47f7883a650a\n", "Some weights of the model checkpoint at facebook/wav2vec2-base were not used when initializing Wav2Vec2ForCTC: ['quantizer.codevectors', 'project_hid.weight', 'quantizer.weight_proj.bias', 'project_q.weight', 'project_q.bias', 'project_hid.bias', 'quantizer.weight_proj.weight']\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 and are newly initialized: ['lm_head.bias', 'lm_head.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ], "source": [ "from transformers import AutoModelForCTC\n", "\n", "model = AutoModelForCTC.from_pretrained(\n", " model_checkpoint, \n", " ctc_loss_reduction=\"mean\", \n", " pad_token_id=processor.tokenizer.pad_token_id,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "2PDAoxYzwb-U" }, "source": [ "The first component of most transformer-based speech models consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the [paper](https://arxiv.org/pdf/2006.13979.pdf) does not need to be fine-tuned anymore. \n", "Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part." ] }, { "cell_type": "markdown", "metadata": { "id": "lD4aGhQM0K-D" }, "source": [ "In a final step, we define all parameters related to training. \n", "To give more explanation on some of the parameters:\n", "- `group_by_length` makes training more efficient by grouping training samples of similar input length into one batch. This can significantly speed up training time by heavily reducing the overall number of useless padding tokens that are passed through the model\n", "- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Timit dataset and might be suboptimal for other speech datasets.\n", "\n", "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n", "\n", "During training, a checkpoint will be uploaded asynchronously to the hub every 400 training steps. It allows you to also play around with the demo widget even while your model is still training.\n", "\n", "**Note**: If one does not want to upload the model checkpoints to the hub, simply set `push_to_hub=False`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KbeKSV7uzGPP", "outputId": "4dcce61c-fc4b-44bb-a774-70ff20971031" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "PyTorch: setting up devices\n", "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n" ] } ], "source": [ "from transformers import TrainingArguments\n", "\n", "training_args = TrainingArguments(\n", " output_dir=repo_name,\n", " group_by_length=True,\n", " per_device_train_batch_size=32,\n", " evaluation_strategy=\"steps\",\n", " num_train_epochs=30,\n", " fp16=True,\n", " gradient_checkpointing=True,\n", " save_steps=500,\n", " eval_steps=500,\n", " logging_steps=500,\n", " learning_rate=1e-4,\n", " weight_decay=0.005,\n", " warmup_steps=1000,\n", " save_total_limit=2,\n", " push_to_hub=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "OsW-WZcL1ZtN" }, "source": [ "Now, all instances can be passed to Trainer and we are ready to start training!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rY7vBmFCPFgC", "outputId": "191b231d-70c1-4315-bdd6-ea2a5d106537" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/content/wav2vec2-base-timit-demo-colab is already a clone of https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-colab. Make sure you pull the latest changes with `repo.git_pull()`.\n", "Using amp fp16 backend\n" ] } ], "source": [ "from transformers import Trainer\n", "\n", "trainer = Trainer(\n", " model=model,\n", " data_collator=data_collator,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=timit[\"train\"],\n", " eval_dataset=timit[\"test\"],\n", " tokenizer=processor.feature_extractor,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "UoXBx1JAA0DX" }, "source": [ "\n", "\n", "---\n", "\n", "${}^1$ To allow models to become independent of the speaker rate, in CTC, consecutive tokens that are identical are simply grouped as a single token. However, the encoded labels should not be grouped when decoding since they don't correspond to the predicted tokens of the model, which is why the `group_tokens=False` parameter has to be passed. If we wouldn't pass this parameter a word like `\"hello\"` would incorrectly be encoded, and decoded as `\"helo\"`.\n", "\n", "${}^2$ The blank token allows the model to predict a word, such as `\"hello\"` by forcing it to insert the blank token between the two l's. A CTC-conform prediction of `\"hello\"` of our model would be `[PAD] [PAD] \"h\" \"e\" \"e\" \"l\" \"l\" [PAD] \"l\" \"o\" \"o\" [PAD]`." ] }, { "cell_type": "markdown", "metadata": { "id": "rpvZHM1xReIW" }, "source": [ "### Training" ] }, { "cell_type": "markdown", "metadata": { "id": "j-3oKSzZ1hGq" }, "source": [ "Training will take a couple of hours depending on the GPU allocated to this notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "_UEjJqGsQw24", "outputId": "e76688f4-e32d-4de2-e3cb-fa1b942a93e6" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running training *****\n", " Num examples = 3978\n", " Num Epochs = 30\n", " Instantaneous batch size per device = 32\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 1\n", " Total optimization steps = 3750\n" ] }, { "data": { "text/html": [ "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1357: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n", " args.max_grad_norm,\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-500\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-500/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-500/preprocessor_config.json\n", "Configuration saved in wav2vec2-base-timit-demo-colab/preprocessor_config.json\n", "/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1357: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n", " args.max_grad_norm,\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-1000\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-1000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-1000/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-1000/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-1500\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-1500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-1500/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-1500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-2000\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-1000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-2500\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-2500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-2500/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-2500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-1500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-3000\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-3000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-3000/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-3000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-2000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-colab/checkpoint-3500\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-3500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-colab/checkpoint-3500/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-timit-demo-colab/checkpoint-3500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-colab/checkpoint-2500] due to args.save_total_limit\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=3750, training_loss=0.0719044921875, metrics={'train_runtime': 6384.5833, 'train_samples_per_second': 18.692, 'train_steps_per_second': 0.587, 'total_flos': 3.098741829539622e+18, 'train_loss': 0.0719044921875, 'epoch': 30.0})" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "UCyp-v3n4Zlt" }, "source": [ "The final WER should be around 0.3 which is reasonable given that state-of-the-art phoneme error rates (PER) are just below 0.1 (see [leaderboard](https://paperswithcode.com/sota/speech-recognition-on-timit)) and that WER is usually worse than PER.\n", "\n", "You can now upload the result of the training to the Hub, just execute this instruction:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 351, "referenced_widgets": [ "853be7d9a3d24adfbdc9a07d181425cc", "76c3fdccb19945e194c441769a1f53b0", "17a09ff9015f4eb2b91ee77b1f7f76e0", "9528d69e9b0549f59f84e94f34e5a583", "8665ac85023c43c3a1599c381ab07ffa", "3b2bcaf5a8f34426bb98ec435603b295", "a5b0355d9a2e4125b7244ae5582212c8", "ea368ae3cbb54a73a06d6fdf30457c02", "4b6973c261204b2bb27596bfe26aea7d", "d21e46e22de04ebe9e02b44e71de04f2", "d9813a20998b4f739de3fa1592fa6f6c", "e9700e09f87a4884b1bdec05bd953c87", "6ab335fdf69045a7a183225814ac50b4", "a7e7d95121934bd4b3cf322636b5ec52", "e62e6b3087364568b2919536eed930b4", "a60bf0d288ce49f39ce301698528515a", "44acbd5e884948b5929f15336986a08e", "0a7e1500d4764332aae441c0afbe12bf", "5b1c1ad9f82d491d98e71923057a6181", "78f37dd782e643d0985b47de288eb6a6", "d578ce02e48c49d7838c002d1d2217fb", "47ae20ba4e5946debdd789af83114d3e" ] }, "id": "JYGQYBhHNsvj", "outputId": "69be0c57-b143-4676-ed55-9c64d30ff973" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Saving model checkpoint to wav2vec2-base-timit-demo-colab\n", "Configuration saved in wav2vec2-base-timit-demo-colab/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-colab/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-timit-demo-colab/preprocessor_config.json\n", "Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "853be7d9a3d24adfbdc9a07d181425cc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.36k/360M [00:00 main\n", "\n", "Dropping the following result as it does not have all the necessary field:\n", "{}\n", "To https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-colab\n", " 2998ea6..431ef82 main -> main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-colab/commit/2998ea690ec1ba32370f856fb558cf22dcb0e119'" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "markdown", "metadata": { "id": "djzwS5WeNu16" }, "source": [ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:" ] }, { "cell_type": "markdown", "metadata": { "id": "Adm0LngNNxq7" }, "source": [ "```python\n", "from transformers import AutoModelForCTC, Wav2Vec2Processor\n", "\n", "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-colab\")\n", "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-colab\")\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "Cw0_ygXSw_MT" }, "source": [ "To fine-tune larger models on larger datasets using CTC loss, one should take a look at the official speech-recognition examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#connectionist-temporal-classification-without-language-model-ctc-wo-lm) 🤗." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "include_colab_link": true, "machine_shape": "hm", "name": "Fine-Tune Speech Recognition Model with 🤗 Transformers using CTC.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "01ae96853d54497f9108870821c45470": { "model_module": "@jupyter-widgets/controls", 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