feature_engineering_pipeline_fine_tune_whisper.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"75b58048-7d14-4fc6-8085-1fc08c81b4a6"},"source":["# Fine-Tune Whisper For Italian ASR with 🤗 Transformers - Feature Engineering Pipeline"],"id":"75b58048-7d14-4fc6-8085-1fc08c81b4a6"},{"cell_type":"markdown","metadata":{"id":"afe0d503-ae4e-4aa7-9af4-dbcba52db41e"},"source":["## Introduction"],"id":"afe0d503-ae4e-4aa7-9af4-dbcba52db41e"},{"cell_type":"markdown","metadata":{"id":"9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0"},"source":["Whisper is a pre-trained model for automatic speech recognition (ASR) \n","published in [September 2022](https://openai.com/blog/whisper/) by the authors \n","Alec Radford et al. from OpenAI. Unlike many of its predecessors, such as \n","[Wav2Vec 2.0](https://arxiv.org/abs/2006.11477), which are pre-trained \n","on un-labelled audio data, Whisper is pre-trained on a vast quantity of \n","**labelled** audio-transcription data, 680,000 hours to be precise. \n","This is an order of magnitude more data than the un-labelled audio data used \n","to train Wav2Vec 2.0 (60,000 hours). What is more, 117,000 hours of this \n","pre-training data is multilingual ASR data. This results in checkpoints \n","that can be applied to over 96 languages, many of which are considered \n","_low-resource_.\n","\n","When scaled to 680,000 hours of labelled pre-training data, Whisper models \n","demonstrate a strong ability to generalise to many datasets and domains.\n","The pre-trained checkpoints achieve competitive results to state-of-the-art \n","ASR systems, with near 3% word error rate (WER) on the test-clean subset of \n","LibriSpeech ASR and a new state-of-the-art on TED-LIUM with 4.7% WER (_c.f._ \n","Table 8 of the [Whisper paper](https://cdn.openai.com/papers/whisper.pdf)).\n","The extensive multilingual ASR knowledge acquired by Whisper during pre-training \n","can be leveraged for other low-resource languages; through fine-tuning, the \n","pre-trained checkpoints can be adapted for specific datasets and languages \n","to further improve upon these results. We'll show just how Whisper can be fine-tuned \n","for low-resource languages in this Colab."],"id":"9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0"},{"cell_type":"markdown","metadata":{"id":"e59b91d6-be24-4b5e-bb38-4977ea143a72"},"source":["<figure>\n","<img src=\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/whisper_architecture.svg\" alt=\"Trulli\" style=\"width:100%\">\n","<figcaption align = \"center\"><b>Figure 1:</b> Whisper model. The architecture \n","follows the standard Transformer-based encoder-decoder model. A \n","log-Mel spectrogram is input to the encoder. The last encoder \n","hidden states are input to the decoder via cross-attention mechanisms. The \n","decoder autoregressively predicts text tokens, jointly conditional on the \n","encoder hidden states and previously predicted tokens. Figure source: \n","<a href=\"https://openai.com/blog/whisper/\">OpenAI Whisper Blog</a>.</figcaption>\n","</figure>"],"id":"e59b91d6-be24-4b5e-bb38-4977ea143a72"},{"cell_type":"markdown","metadata":{"id":"21b6316e-8a55-4549-a154-66d3da2ab74a"},"source":["The Whisper checkpoints come in five configurations of varying model sizes.\n","The smallest four are trained on either English-only or multilingual data.\n","The largest checkpoint is multilingual only. All nine of the pre-trained checkpoints \n","are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The \n","checkpoints are summarised in the following table with links to the models on the Hub:\n","\n","| Size   | Layers | Width | Heads | Parameters | English-only                                         | Multilingual                                      |\n","|--------|--------|-------|-------|------------|------------------------------------------------------|---------------------------------------------------|\n","| tiny   | 4      | 384   | 6     | 39 M       | [✓](https://huggingface.co/openai/whisper-tiny.en)   | [✓](https://huggingface.co/openai/whisper-tiny.)  |\n","| base   | 6      | 512   | 8     | 74 M       | [✓](https://huggingface.co/openai/whisper-base.en)   | [✓](https://huggingface.co/openai/whisper-base)   |\n","| small  | 12     | 768   | 12    | 244 M      | [✓](https://huggingface.co/openai/whisper-small.en)  | [✓](https://huggingface.co/openai/whisper-small)  |\n","| medium | 24     | 1024  | 16    | 769 M      | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |\n","| large  | 32     | 1280  | 20    | 1550 M     | x                                                    | [✓](https://huggingface.co/openai/whisper-large)  |\n","\n","For demonstration purposes, we'll fine-tune the multilingual version of the \n","[`\"small\"`](https://huggingface.co/openai/whisper-small) checkpoint with 244M params (~= 1GB). \n","As for our data, we'll train and evaluate our system on a low-resource language \n","taken from the [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)\n","dataset. We'll show that with as little as 8 hours of fine-tuning data, we can achieve \n","strong performance in this language."],"id":"21b6316e-8a55-4549-a154-66d3da2ab74a"},{"cell_type":"markdown","metadata":{"id":"3a680dfc-cbba-4f6c-8a1f-e1a5ff3f123a"},"source":["------------------------------------------------------------------------\n","\n","\\\\({}^1\\\\) The name Whisper follows from the acronym “WSPSR”, which stands for “Web-scale Supervised Pre-training for Speech Recognition”."],"id":"3a680dfc-cbba-4f6c-8a1f-e1a5ff3f123a"},{"cell_type":"markdown","metadata":{"id":"55fb8d21-df06-472a-99dd-b59567be6dad"},"source":["## Prepare Environment"],"id":"55fb8d21-df06-472a-99dd-b59567be6dad"},{"cell_type":"markdown","metadata":{"id":"9cd52dc1-ade1-44bb-a2d7-2ed98f110fed"},"source":["First, we need to update the Unix package `ffmpeg` to version 4:"],"id":"9cd52dc1-ade1-44bb-a2d7-2ed98f110fed"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":34265,"status":"ok","timestamp":1670334495804,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"},"user_tz":-60},"id":"69ee227d-60c5-44bf-b04d-c2092f997454","outputId":"c1c505b9-c54b-4a16-dc1d-6a915bf02cc6"},"outputs":[{"output_type":"stream","name":"stdout","text":["\r0% [Working]\r            \rGet:1 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n","\r            \rGet:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n","\r0% [Connecting to archive.ubuntu.com] [1 InRelease 14.2 kB/88.7 kB 16%] [Waitin\r0% [2 InRelease gpgv 3,626 B] [Connecting to archive.ubuntu.com] [1 InRelease 1\r                                                                               \rIgn:3 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n","\r0% [2 InRelease gpgv 3,626 B] [Connecting to archive.ubuntu.com (185.125.190.36\r0% [2 InRelease gpgv 3,626 B] [Connecting to archive.ubuntu.com (185.125.190.36\r                                                                               \rGet:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease [1,581 B]\n","Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n","Get:6 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n","Hit:7 http://archive.ubuntu.com/ubuntu bionic InRelease\n","Get:8 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n","Get:9 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 Packages [22.9 kB]\n","Get:10 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [3,094 kB]\n","Get:11 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [1,056 kB]\n","Hit:12 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n","Get:13 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,567 kB]\n","Get:14 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [1,307 kB]\n","Hit:15 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n","Get:17 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [83.3 kB]\n","Get:18 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB]\n","Get:19 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic InRelease [15.9 kB]\n","Get:20 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [2,234 kB]\n","Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [30.0 kB]\n","Get:22 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [3,519 kB]\n","Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,342 kB]\n","Get:24 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [1,347 kB]\n","Get:25 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [1,143 kB]\n","Get:26 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [40.8 kB]\n","Get:27 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic/main amd64 Packages [12.5 kB]\n","Fetched 18.0 MB in 4s (4,643 kB/s)\n","Reading package lists... 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(3.0.1-0york0~18.04) ...\n","Setting up libpocketsphinx3:amd64 (0.8.0+real5prealpha-1ubuntu2) ...\n","Setting up libpostproc55:amd64 (7:4.3.2-0york0~18.04) ...\n","Setting up libavresample4:amd64 (7:4.3.2-0york0~18.04) ...\n","Setting up libsrt1-gnutls:amd64 (1.4.1-5~18.04.york0) ...\n","Setting up libmysofa1:amd64 (1.0~dfsg0-2~18.04.york0) ...\n","Setting up libvidstab1.1:amd64 (1.1.0-2~18.04.york1) ...\n","Setting up libcodec2-0.7:amd64 (0.7-1) ...\n","Setting up librabbitmq4:amd64 (0.8.0-1ubuntu0.18.04.2) ...\n","Setting up libaom0:amd64 (1.0.0.errata1-3~18.04.york0) ...\n","Setting up libserd-0-0:amd64 (0.28.0~dfsg0-1) ...\n","Setting up libx265-192:amd64 (3.4-0york0~18.04) ...\n","Setting up libswscale5:amd64 (7:4.3.2-0york0~18.04) ...\n","Setting up libswresample3:amd64 (7:4.3.2-0york0~18.04) ...\n","Setting up libsord-0-0:amd64 (0.16.0~dfsg0-1) ...\n","Setting up libsratom-0-0:amd64 (0.6.0~dfsg0-1) ...\n","Setting up libavcodec58:amd64 (7:4.3.2-0york0~18.04) ...\n","Setting up liblilv-0-0 (0.24.2~dfsg0-1) ...\n","Setting up libavformat58:amd64 (7:4.3.2-0york0~18.04) ...\n","Setting up libavfilter7:amd64 (7:4.3.2-0york0~18.04) ...\n","Setting up libavdevice58:amd64 (7:4.3.2-0york0~18.04) ...\n","Setting up ffmpeg (7:4.3.2-0york0~18.04) ...\n","Removing obsolete conffile /etc/ffserver.conf ...\n","Processing triggers for libc-bin (2.27-3ubuntu1.6) ...\n","Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"]}],"source":["!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n","!apt update\n","!apt install -y ffmpeg"],"id":"69ee227d-60c5-44bf-b04d-c2092f997454"},{"cell_type":"markdown","metadata":{"id":"1d85d613-1c7e-46ac-9134-660bbe7ebc9d"},"source":["We'll employ several popular Python packages to prepare our features for fine-tuning whisper.\n","We'll use `datasets` to download and prepare our training data and \n","`transformers` to load and train our Whisper model. We'll also require\n","the `librosa` package to pre-process audio files."],"id":"1d85d613-1c7e-46ac-9134-660bbe7ebc9d"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":54915,"status":"ok","timestamp":1670338415623,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"},"user_tz":-60},"id":"e68ea9f8-9b61-414e-8885-3033b67c2850","outputId":"9bcc1527-1714-4694-e9f2-3d62e07dc686"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting git+https://github.com/huggingface/transformers\n","  Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-h2d2evnw\n","  Running command git clone -q https://github.com/huggingface/transformers /tmp/pip-req-build-h2d2evnw\n","  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n","Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n","  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n","\u001b[K     |████████████████████████████████| 7.6 MB 29.0 MB/s \n","\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers==4.26.0.dev0) (21.3)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers==4.26.0.dev0) (6.0)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers==4.26.0.dev0) (2022.6.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers==4.26.0.dev0) (2.23.0)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers==4.26.0.dev0) (4.64.1)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers==4.26.0.dev0) (1.21.6)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from transformers==4.26.0.dev0) (0.11.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers==4.26.0.dev0) (3.8.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers==4.26.0.dev0) (4.1.1)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers==4.26.0.dev0) (3.0.9)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers==4.26.0.dev0) (1.25.11)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers==4.26.0.dev0) (2022.9.24)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers==4.26.0.dev0) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers==4.26.0.dev0) (3.0.4)\n","Building wheels for collected packages: transformers\n","  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for transformers: filename=transformers-4.26.0.dev0-py3-none-any.whl size=5884245 sha256=72b3c5eb2c3e12807dfaafb551e536a55758d8c6bc98c2f4e9be06635b2524bf\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-10ruzvtl/wheels/42/68/45/c63edff61c292f2dfd4df4ef6522dcbecc603e7af82813c1d7\n","Successfully built transformers\n","Installing collected packages: tokenizers, transformers\n","Successfully installed tokenizers-0.13.2 transformers-4.26.0.dev0\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: librosa in /usr/local/lib/python3.8/dist-packages (0.8.1)\n","Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from librosa) (4.4.2)\n","Requirement already satisfied: soundfile>=0.10.2 in /usr/local/lib/python3.8/dist-packages (from librosa) (0.11.0)\n","Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.8/dist-packages (from librosa) (1.2.0)\n","Requirement already satisfied: numba>=0.43.0 in /usr/local/lib/python3.8/dist-packages (from librosa) (0.56.4)\n","Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from librosa) (3.0.0)\n","Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.8/dist-packages (from librosa) (1.6.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from librosa) (21.3)\n","Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.8/dist-packages (from librosa) (0.4.2)\n","Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from librosa) (1.7.3)\n","Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from librosa) (1.0.2)\n","Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.8/dist-packages (from librosa) (1.21.6)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.8/dist-packages (from numba>=0.43.0->librosa) (4.13.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from numba>=0.43.0->librosa) (57.4.0)\n","Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.8/dist-packages (from numba>=0.43.0->librosa) (0.39.1)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->librosa) (3.0.9)\n","Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from pooch>=1.0->librosa) (2.23.0)\n","Requirement already satisfied: appdirs>=1.3.0 in /usr/local/lib/python3.8/dist-packages (from pooch>=1.0->librosa) (1.4.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2022.9.24)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (1.25.11)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa) (3.1.0)\n","Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.8/dist-packages (from soundfile>=0.10.2->librosa) (1.15.1)\n","Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa) (2.21)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata->numba>=0.43.0->librosa) (3.10.0)\n"]}],"source":["!pip install datasets>=2.6.1\n","!pip install git+https://github.com/huggingface/transformers\n","!pip install librosa"],"id":"e68ea9f8-9b61-414e-8885-3033b67c2850"},{"cell_type":"markdown","metadata":{"id":"b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0"},"source":["## Load Dataset"],"id":"b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0"},{"cell_type":"markdown","metadata":{"id":"674429c5-0ab4-4adf-975b-621bb69eca38"},"source":["Using 🤗 Datasets, downloading and preparing data is extremely simple. \n","We can download and prepare the Common Voice splits in just one line of code. \n","\n","First, ensure you have accepted the terms of use on the Hugging Face Hub: [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). Once you have accepted the terms, you will have full access to the dataset and be able to download the data locally.\n","\n","Since Italian is a very big datset, we'll take only 10% of both its `test` and `train` split to give approximately 4 hours of training data. Even if it is a small percentage we'll see that the model performs very well."],"id":"674429c5-0ab4-4adf-975b-621bb69eca38"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["9374b2fa3ff34a828b3ff9041a7ea173","90698c8c395d46feac323ea2a1822109","a03e589588b34ff4aadef5a18d0360c0","c599caa187c341b282b4d3b90363d9ea","c88707fe5d7b4637a715e7b104e230ca","19aba22aab2f42c385b8d4c6ab1eecbb","dc4081c55bcf444c9a77c6e46255d38c","68d05b658bd84396acd2739c81b0fa81","4824079c797a44f6a6b5e91538e2ad6b","c65e77732c6b459b881ed595a07926c8","fa18d9cf7b004ef7af6e08bdc4ab1fe6","dd2b783c1ccc465bb6e758c35290aacf","f46fa45fb6eb49378496b6a30e42ed0e","9ba355467fbf4818a1bcdde1ac0eafe8","3b386ca67179446c9f001b3025c3c2cf","1aa902cdec884f50ae83717cb1ff71cb","6c7effa8723d4ae8a6aace7b9bae067c","b0f4bf5402cd4ef8894f842bbecb1116","6cfff70c815d4f4cbd597991d13071a5","e4a0290a62974910a2a8d0f5e9183e65","165cd81f6e5342b9a1260d59e383a966","eb6d2ef491bd4886a159bd5a216afd06","5667513393b34262b8b6c09165b9b32c","108e082741e341c5a77707c5367c1260","35a14039609f4c248d9ff9740ab2f898","a89a357eade645a68cbadf670d3b44ce","e4c40e5fa8f84d54b484c413f7e159c1","98bb40da4911440d9ea246fb672f2f92","7c7328b854b8461a8c5e282cf9e39b45","eb1019b4dc8c4a9096d2ab80791ce7e3","109636a46c8b4e6b9a36f9473b986d17","b3fc6c443ae945a8a09ac7ef7b650437","6a847e2ada5543a9833230c71bf961ac","3d2d0f4b25324bf6ac4785a856d1239a","9e2db44430fa4e7990616d6fa21efa56","21bb877a61154785886b9e4004bb6a29","606f9c0aea8747f48c240e9325f26f4a","a6a3e25282594a16ac6bff8cb5a1e88c","6799f6b0f2e54c9eb4b4771755ca8909","4e464026818f443e92453b8a19e8e64e","13a208915e804a14832420f0e7813e4c","3059d19e185f4835a789b9ed55b31af2","f4e4fedf94754a488f0285d150dfeab2","9badedf9ee7a424683092bb28b8968aa","94a462f2312b4e53b94928e42fe574bc","82ca1b73524d453b94cb401482a49cc1","e70625e632e346b5bf8e546cf5076d5f","3f4bef565c9e4ec2b88fa2c9cce92004","60d4a83ea0834327ab49ca64ff536e12","28edf6ff439743bbbc6c43dbe57cca58","9ac6fc806c7747ad9cb927bf51cfdf8d","4104f5fd31004c6f9ca2b984a7ab44b7","3c01cf8ec8f64b1c9413e3a41f03b516","7ffbf9a4169d422db62280cdc4093357","1b2c3482478d4f9189bdfa297d882678","ec2d4613937a416b90aa58807b89bc8d","470535c5b2d54b708d905e81448075b3","2e6e4bc42e6e432e90385a7ff66443e2","602ab3771473455a8e46872755d40f29","16e35638b4354ec7a4fbbc6cb87195a1","65cb0bf9ad734da0b30745b1d1271523","4c65fab221504c0b98d2628efc5e34c2","1d790f34418641f3a5201e0dd4086c84","85ecbe6487fb4f2faa0ecb9f89363d32","e4817cf36fe949fdbc9d34cc58f437da","3ce0c4f7710b49128a0f5a662897192d","7131565055ac44d5a7bc52831ad2e259","896dd160a84f49f389ba68bbc244a929","82a7a034ef174bb4abfaf2974cc9df1e","07349ea342324925aaef42c1e32d5770","5a07ba1e345b46b1b7d59832fc8ce392","b8fc16fe1420497a9cd943fb7cb80bdb","2db628e0eafa464bb142ef90e937ec0f","6112d35da5cc413bb775be54655932ef","250b2cb01203483aa29664c24fc4bd1b","74b3f47cc5a8433fbcea9f7c1ea928b6","ddc10aac4410478e865f3ba2521c8be9","a64cf507363c460d98dbf182f60f9823","64a99c239c3a4da784e2f71f600a6358","d8b49f4457bf4773b2d0ad5375031688","7291eba9943d4ce3914212920c2a4e3c","7f94a4bb938e409b9dd80f26edafab6d","eb67c1ebe2cd44ceb370122dd074ec75","3728f62d68d54195b84486caea024385","3fe5427db8a3460da4c9a88e7577f9f3","fe6b3a5c9941405c8f574a6330f3587f","a82811d08bd14fa6a66ff5aac017a673","323db52131d449dd818c14c28ed51b2e","82994f3bc2ea4f31a90ce183769463a1","a3c9949c098340e5a60ee98d02e35ed8","87df454fc0f847fe96077e0728a2bf32","307172e3b4a142a78fe9442883c1d9b9","a63972de8d9a4fb3a9bb016906ba1126","8f83f49dda7c40f18920c3ea03510700","c4d1a8027fec42b4b10d1f6edf50593c","44f5be8cae7b472793550bb246e482a5","4e70098f730846699283aee5cb365218","19670e3a892c4a9ba4a16d5b401c8249","533c931b0d5d4e64ad3817a1e7bcd9a3","4847e3baa43242e0bd0456d94fa0f69e","20b490e2a1dc4728a80ae391b7c44548","580d1fc61cb8482da44fafcb88e6e011","016b99ee8ea24afe95c34be61b26f799","4a626816598f4c93aee13ed1264626dd","3201b666b79948c49bdabe64642e333b","14d645fd3fa840a38ab93d33ec07c97c","36fdfe2ccf7d44d08953e6a5a8a606a6","23f6b770799742f6b29ab5746e68e072","765837d35f6a4b07b64178383aaedb10","eadc9db6eee543d5911c1416eaf3e656","490bbd7750614364a36bca017d71ecca","4d4f3cb33e1741698045ffe33c34322e","0b80e763973f4d28aad390cae37a0795","cc18ae1148e6499488ef329689388a7b","832ea710a1084b548190bb9eb9cd160f","f2ca0895fb914058815577d2e1a92300","cd5953c2e41748ea9073e2828570ff03","05650c78ad0346dbafc30baf1daaf526","c28e51922f6b4cf0b8340206636e1008","24af994e4690420796049daccfa94e90","168a14ff7c6a4da1aedd9985dacdd681","f7ae2b58f847479faf23c960e3c3135a","b24ef7d7b88948a7a1365342be68fb6b","5a191465941b4bff9fa3c95d9700ff71","0bdcfcee9f2a4540a8a6b2ab4a8dba52","f33024ad8c004f24a182e70ba268fb8a","67b32c7d0d3747a8b5097562badf863a","54d2726eeece4c59951e3d31dffe5809","ee6b26e97a534199a1a9bcb683254cec","add039c8f72746e993c68b1a4b7c2029","ed1cd16253ee41c0b09a64be747b60f4","9cdcefcf77cf45febaea6754a79b641b","61a00abb66174b21a9d3cbf947842ac0","4983a7339b574f62b9bf1c7d64646418","b68e3ba53db244efbc3844149b58698d","8530c2b1f4a34ed18453350a3664775c","64f226e46384431892073f366879eab8","38f5f7266c5c47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[00:00, 96778.92it/s]\u001b[A\n","Reading metadata...: 56493it [00:00, 105111.95it/s]\u001b[A\n","Reading metadata...: 67220it [00:00, 103510.71it/s]\u001b[A\n","Reading metadata...: 79786it [00:00, 110387.20it/s]\u001b[A\n","Reading metadata...: 91636it [00:00, 112878.52it/s]\u001b[A\n","Reading metadata...: 104004it [00:00, 116170.69it/s]\u001b[A\n","Reading metadata...: 115697it [00:01, 113982.77it/s]\u001b[A\n","Reading metadata...: 127910it [00:01, 116408.67it/s]\u001b[A\n","Reading metadata...: 152609it [00:01, 110202.84it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Generating validation split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"180aa16f71c5496ab8d23674975dde00"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\n","\n","Reading metadata...: 0it [00:00, ?it/s]\u001b[A\u001b[A\n","\n","Reading metadata...: 14997it [00:00, 109879.17it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Generating test split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b26960e2a5ad4ee795db7d673e35dd9f"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\n","\n","\n","Reading metadata...: 0it [00:00, ?it/s]\u001b[A\u001b[A\u001b[A\n","\n","\n","Reading metadata...: 15003it [00:00, 119265.58it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Generating other split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"24e74d0d82b541a3885093a889a31706"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\n","Reading metadata...: 27it [00:00, 46951.16it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Generating invalidated split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bb9fbff283844697b35437fcd0224581"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\n","\n","\n","Reading metadata...: 0it [00:00, ?it/s]\u001b[A\u001b[A\u001b[A\n","\n","\n","Reading metadata...: 17476it [00:00, 106046.19it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Dataset common_voice_11_0 downloaded and prepared to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice_11_0/it/11.0.0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f. Subsequent calls will reuse this data.\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Found cached dataset common_voice_11_0 (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice_11_0/it/11.0.0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f)\n"]},{"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'],\n","        num_rows: 15261\n","    })\n","    test: Dataset({\n","        features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n","        num_rows: 1500\n","    })\n","})\n"]}],"source":["from datasets import load_dataset, DatasetDict\n","\n","common_voice = DatasetDict()\n","\n","common_voice[\"train\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"it\", split=\"train[:10%]\", use_auth_token=True)\n","common_voice[\"test\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"it\", split=\"test[:10%]\", use_auth_token=True)\n","\n","print(common_voice)"],"id":"a2787582-554f-44ce-9f38-4180a5ed6b44"},{"cell_type":"markdown","metadata":{"id":"d5c7c3d6-7197-41e7-a088-49b753c1681f"},"source":["Most ASR datasets only provide input audio samples (`audio`) and the \n","corresponding transcribed text (`sentence`). Common Voice contains additional \n","metadata information, such as `accent` and `locale`, which we can disregard for ASR.\n","Keeping the notebook as general as possible, we only consider the input audio and\n","transcribed text for fine-tuning, discarding the additional metadata information:"],"id":"d5c7c3d6-7197-41e7-a088-49b753c1681f"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":181,"status":"ok","timestamp":1670335226415,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"},"user_tz":-60},"id":"20ba635d-518c-47ac-97ee-3cad25f1e0ce","outputId":"6820ec92-12fc-4cd6-9a44-f3a6453eefc1"},"outputs":[{"output_type":"stream","name":"stdout","text":["DatasetDict({\n","    train: Dataset({\n","        features: ['audio', 'sentence'],\n","        num_rows: 15261\n","    })\n","    test: Dataset({\n","        features: ['audio', 'sentence'],\n","        num_rows: 1500\n","    })\n","})\n"]}],"source":["common_voice = common_voice.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"])\n","\n","print(common_voice)"],"id":"20ba635d-518c-47ac-97ee-3cad25f1e0ce"},{"cell_type":"markdown","metadata":{"id":"2d63b2d2-f68a-4d74-b7f1-5127f6d16605"},"source":["## Prepare Feature Extractor, Tokenizer and Data"],"id":"2d63b2d2-f68a-4d74-b7f1-5127f6d16605"},{"cell_type":"markdown","metadata":{"id":"601c3099-1026-439e-93e2-5635b3ba5a73"},"source":["The ASR pipeline can be de-composed into three stages: \n","1) A feature extractor which pre-processes the raw audio-inputs\n","2) The model which performs the sequence-to-sequence mapping \n","3) A tokenizer which post-processes the model outputs to text format\n","\n","In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer, \n","called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)\n","and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer) \n","respectively.\n","\n","We'll go through details for setting-up the feature extractor and tokenizer one-by-one!"],"id":"601c3099-1026-439e-93e2-5635b3ba5a73"},{"cell_type":"markdown","metadata":{"id":"560332eb-3558-41a1-b500-e83a9f695f84"},"source":["### Load WhisperFeatureExtractor"],"id":"560332eb-3558-41a1-b500-e83a9f695f84"},{"cell_type":"markdown","metadata":{"id":"32ec8068-0bd7-412d-b662-0edb9d1e7365"},"source":["The Whisper feature extractor performs two operations:\n","1. Pads / truncates the audio inputs to 30s: any audio inputs shorter than 30s are padded to 30s with silence (zeros), and those longer that 30s are truncated to 30s\n","2. Converts the audio inputs to _log-Mel spectrogram_ input features, a visual representation of the audio and the form of the input expected by the Whisper model"],"id":"32ec8068-0bd7-412d-b662-0edb9d1e7365"},{"cell_type":"markdown","metadata":{"id":"589d9ec1-d12b-4b64-93f7-04c63997da19"},"source":["<figure>\n","<img src=\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/spectrogram.jpg\" alt=\"Trulli\" style=\"width:100%\">\n","<figcaption align = \"center\"><b>Figure 2:</b> Conversion of sampled audio array to log-Mel spectrogram.\n","Left: sampled 1-dimensional audio signal. Right: corresponding log-Mel spectrogram. Figure source:\n","<a href=\"https://ai.googleblog.com/2019/04/specaugment-new-data-augmentation.html\">Google SpecAugment Blog</a>.\n","</figcaption>"],"id":"589d9ec1-d12b-4b64-93f7-04c63997da19"},{"cell_type":"markdown","metadata":{"id":"b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa"},"source":["We'll load the feature extractor from the pre-trained checkpoint with the default values:"],"id":"b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa"},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":7656,"status":"ok","timestamp":1670335251601,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"},"user_tz":-60},"id":"bc77d7bb-f9e2-47f5-b663-30f7a4321ce5","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["571525a78fad4e57aa81711f61fcfe3f","c9afc774f82e43628f03f9c377d48d12","c55346d7180148129f492bd579aefbfa","099c1829c1bd46f48b8b734209935776","9d5243aa0b3d48c49ae6a92ca20f7edf","a3895470a0d24f038587f9ae85b9ec68","b153741d867e4f0a8dfd37d29685dfb1","e6d9ae14c97e4d78b5e3e29342a82c61","8416302dc8a541d8aa58cc735a2a5d58","f9f9c6e504744c1da34cf57c8e85bf9e","d0a26a080491498e99ddbcf9b02b763d"]},"outputId":"47f6d0a0-97ab-42b4-fd35-7c887f40b48c"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/185k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"571525a78fad4e57aa81711f61fcfe3f"}},"metadata":{}}],"source":["from transformers import WhisperFeatureExtractor\n","\n","feature_extractor = WhisperFeatureExtractor.from_pretrained(\"openai/whisper-small\")"],"id":"bc77d7bb-f9e2-47f5-b663-30f7a4321ce5"},{"cell_type":"markdown","metadata":{"id":"93748af7-b917-4ecf-a0c8-7d89077ff9cb"},"source":["### Load WhisperTokenizer"],"id":"93748af7-b917-4ecf-a0c8-7d89077ff9cb"},{"cell_type":"markdown","metadata":{"id":"2bc82609-a9fb-447a-a2af-99597c864029"},"source":["The Whisper model outputs a sequence of _token ids_. The tokenizer maps each of these token ids to their corresponding text string. For Italian, we can load the pre-trained tokenizer and use it for fine-tuning without any further modifications. We simply have to \n","specify the target language and the task. These arguments inform the \n","tokenizer to prefix the language and task tokens to the start of encoded \n","label sequences:"],"id":"2bc82609-a9fb-447a-a2af-99597c864029"},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":2966,"status":"ok","timestamp":1670335324201,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"},"user_tz":-60},"id":"c7b07f9b-ae0e-4f89-98f0-0c50d432eab6","colab":{"base_uri":"https://localhost:8080/","height":209,"referenced_widgets":["f82306ae9c6144d894084631e2709ee2","8ebeb2f9697f41d38c16433339a9317f","b7e9fa5f49cb4ee6bb480c048f34cc3a","a9ca382b89bc4a49920099a4ad9cb127","73d37e95b6ec488db0f93d6706fc29f4","eedb8c01e4ad4844ade6645bb7b3d8ab","3ea39c697a5c4e49aac0342a01773849","8c38cefd301a447cabb25710b350ed66","b387faa82af8438ebbf837814fe67a22","e36e8a5381ff4877b38e1c24e0cf9c59","87371546d4dc423b8606e0348cd9cc44","af47e87548bf4a96a07fc4f5913e3478","d9a1fdfbdf0a4afea695813ded9d7636","0d163e6ad494490d8c40c7a66107e23c","aa73b1004c8e4bf3a8ce80051d59d5f3","a2b3b072f78544b794af6c1a038e284e","c5fdad1305ce425aa564a6c12d997257","11a460970fda4cb5bd9b3e03915e44d7","4738551dc87b413c84bfb60f2858adc2","bf642224f29c4b259e6043a8180915b7","fbe4b002e1754df9a0b0636cd5d49a68","cd2c3ac3e223449da4ed531809943257","627ae008f00e47e2962ba005094f993d","0e74a0bfa5804b9e90830b166bae82b7","8e90e00c5a4345969cc88fbd33647c3a","1a0a9ff1495842319a8379c5aac8fcb7","a2bb20035765428baaed85f7e7ddbea8","8b30a18e347c443f962aa427fb14ee6d","088a7c441f0f42dfa38424ccc73f17a3","d775aaa3a0ae43e081491c1001fbad0d","a130707dfa2b4bd18cb22865870b582d","2dfce35487e94eb0a5b3f3a2a547d71b","ee57a18808f14bc59a7ee1de876b533e","e61f72fa0e4c4b2dbf3d122c28d0a667","713f1ecc4eb64e21861d90e3183c344b","8e96671e824647e4921ea0b3d8583051","a617306469db4945b807ac769cb3adec","6d77244084fa4ba697c2338c539d6328","f113223de3a840188a01df91e7302dd3","3aa7eb16d842486b8b89d5efea13ec71","9e7fdf3620704b1d901120a478a21235","921d6b60663640cabba4ede43c34bbb0","8af1cbf6825847419b0764e45e16456a","3440812677484e30bf32fc0537467e70","98911969be6049b1aabd262509a5e064","b50dc77606504d808beec0533d12ffb0","b83c073fb54f4b508e6b6f3d4ba84e10","0c3c47c7d65340c39e37503ee6f653d2","338128bc1cc948119f7ed44169593f78","aeb6a71718794f8593c7e72228f17527","3daf6f5df6304c478f68afb170d19a5f","d20e6ae487484fe590c00238dd2c6f72","2f049585c421403a88d19ba39fc0f0a4","2522f41ce2c543c98b79234148c82475","94bb7352539149fdb3f777a055cc7467","422e1ed9fe3847b3b20067d4bde84452","e1924ea1d2b4439d9eb9ac6a45075b36","b8d820c6e09a4534b3fe8dc4ffc2a10e","cb2dad240bd24fcdbb686094d1a5c49e","5a7ff570c95540faa262d31354c14f49","7fa4d9612ce04f14b5449ec55342edfc","fb238c33abd4460eb90a434a303ea78c","fa31136fd4424dc4882f22755eeabaf1","bb36aa389f964ca4981a19119dd80113","9ba5ae4efa894da5a4b1e293d11e9d5f","13fc88417de14cabb595eaa395e45551"]},"outputId":"9477319b-edb9-488c-b176-436747d8f3ad"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading: 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e61f72fa0e4c4b2dbf3d122c28d0a667"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/2.11k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"98911969be6049b1aabd262509a5e064"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/2.06k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"422e1ed9fe3847b3b20067d4bde84452"}},"metadata":{}}],"source":["from transformers import WhisperTokenizer\n","\n","tokenizer = WhisperTokenizer.from_pretrained(\"openai/whisper-small\", language=\"Italian\", task=\"transcribe\")"],"id":"c7b07f9b-ae0e-4f89-98f0-0c50d432eab6"},{"cell_type":"markdown","metadata":{"id":"d2ef23f3-f4a8-483a-a2dc-080a7496cb1b"},"source":["### Combine To Create A WhisperProcessor"],"id":"d2ef23f3-f4a8-483a-a2dc-080a7496cb1b"},{"cell_type":"markdown","metadata":{"id":"5ff67654-5a29-4bb8-a69d-0228946c6f8d"},"source":["To simplify using the feature extractor and tokenizer, we can _wrap_ \n","both into a single `WhisperProcessor` class. This processor object \n","inherits from the `WhisperFeatureExtractor` and `WhisperProcessor`, \n","and can be used on the audio inputs and model predictions as required. \n","In doing so, we only need to keep track of two objects during training: \n","the `processor` and the `model`:"],"id":"5ff67654-5a29-4bb8-a69d-0228946c6f8d"},{"cell_type":"code","execution_count":null,"metadata":{"id":"77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"},"outputs":[],"source":["from transformers import WhisperProcessor\n","\n","processor = WhisperProcessor.from_pretrained(\"openai/whisper-small\", language=\"Italian\", task=\"transcribe\")"],"id":"77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"},{"cell_type":"markdown","metadata":{"id":"381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"},"source":["### Prepare Data"],"id":"381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"},{"cell_type":"markdown","metadata":{"id":"9649bf01-2e8a-45e5-8fca-441c13637b8f"},"source":["Let's print the first example of the Common Voice dataset to see \n","what form the data is in:"],"id":"9649bf01-2e8a-45e5-8fca-441c13637b8f"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3236,"status":"ok","timestamp":1670335453026,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"},"user_tz":-60},"id":"6e6b0ec5-0c94-4e2c-ae24-c791be1b2255","outputId":"422b4246-5769-40a2-d25a-73561fc5ec4f"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/305a8b5e120009d1694df089aa5f1b727eec947fd0fd456ddd9929662240dd9a/common_voice_it_20044036.mp3', 'array': array([0.000000e+00, 7.482125e-15, 1.497187e-14, ..., 9.151338e-06,\n","       6.415377e-06, 2.520004e-06], dtype=float32), 'sampling_rate': 48000}, 'sentence': \"Vennero per questo classificati all'ultimo posto.\"}\n"]}],"source":["print(common_voice[\"train\"][0])"],"id":"6e6b0ec5-0c94-4e2c-ae24-c791be1b2255"},{"cell_type":"markdown","metadata":{"id":"5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"},"source":["Since \n","our input audio is sampled at 48kHz, we need to _downsample_ it to \n","16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n","\n","We'll set the audio inputs to the correct sampling rate using dataset's \n","[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n","method. This operation does not change the audio in-place, \n","but rather signals to `datasets` to resample audio samples _on the fly_ the \n","first time that they are loaded:"],"id":"5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"},{"cell_type":"code","execution_count":null,"metadata":{"id":"f12e2e57-156f-417b-8cfb-69221cc198e8"},"outputs":[],"source":["from datasets import Audio\n","\n","common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))"],"id":"f12e2e57-156f-417b-8cfb-69221cc198e8"},{"cell_type":"markdown","metadata":{"id":"00382a3e-abec-4cdd-a54c-d1aaa3ea4707"},"source":["Re-loading the first audio sample in the Common Voice dataset will resample \n","it to the desired sampling rate:"],"id":"00382a3e-abec-4cdd-a54c-d1aaa3ea4707"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":349,"status":"ok","timestamp":1670335468980,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"},"user_tz":-60},"id":"87122d71-289a-466a-afcf-fa354b18946b","outputId":"3581b17a-5d90-47d4-c3bd-ee1454be1f89"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/305a8b5e120009d1694df089aa5f1b727eec947fd0fd456ddd9929662240dd9a/common_voice_it_20044036.mp3', 'array': array([ 2.1445896e-15,  1.2242107e-14, -1.6304207e-14, ...,\n","       -1.1713955e-06,  8.2080269e-06,  1.0825489e-05], dtype=float32), 'sampling_rate': 16000}, 'sentence': \"Vennero per questo classificati all'ultimo posto.\"}\n"]}],"source":["print(common_voice[\"train\"][0])"],"id":"87122d71-289a-466a-afcf-fa354b18946b"},{"cell_type":"markdown","metadata":{"id":"91edc72d-08f8-4f01-899d-74e65ce441fc"},"source":["Now we can write a function to prepare our data ready for the model:\n","1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n","2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n","3. We encode the transcriptions to label ids through the use of the tokenizer."],"id":"91edc72d-08f8-4f01-899d-74e65ce441fc"},{"cell_type":"code","execution_count":null,"metadata":{"id":"6525c478-8962-4394-a1c4-103c54cce170"},"outputs":[],"source":["def prepare_dataset(batch):\n","    # load and resample audio data from 48 to 16kHz\n","    audio = batch[\"audio\"]\n","\n","    # compute log-Mel input features from input audio array \n","    batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n","\n","    # encode target text to label ids \n","    batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n","    return batch"],"id":"6525c478-8962-4394-a1c4-103c54cce170"},{"cell_type":"markdown","metadata":{"id":"70b319fb-2439-4ef6-a70d-a47bf41c4a13"},"source":["We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially."],"id":"70b319fb-2439-4ef6-a70d-a47bf41c4a13"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":181,"referenced_widgets":["9dab51aacacd4747803af4797bc8a5be","5b03d9336b1748a19e1c1d40e8ad0c72","747c5de9fa8c49038d7eb04ebeefc0db","78d42eb655d24177aae1b393d90d85dd","e93dc93d28e8444985c15c0af0238d46","9b22e106884b445dbeb2d1c8a548b784","7526e703c3b143d5872099cacc9318e3","ae7caf6cc7cc45879b4010f2ef471555","cfd762fc735244b79ece98253fbbf2af","11b1cf856e84482e9821d9879fa86f94","0d9d6cfb20dc4f5fbc6f23984faa86af","748887480fe6462a9118c0fd99dfafcd","c18c8a977b904d64a5f96b98d5aa23eb","b351ca5b8cf949fcbde5730eb316b2fe","497f51642d2a4cb3b78b9097c039ddfd","e19d0d455de04700806a6ff2eea622fa","6f4481e58ed7413987551aef4cfd633e","e736468fb5b14763b00dec05cf3fcec1","8a23b643a30447ecb2214f6cd71c0ba3","5f9dfeb1bffe43cf9ce734dc828eab45","f5e77008001742c58b7778d19a58038f","4089cc1dab754a20adc25b3eaf725444","611e68f4e7ac40cf8962baf4e2217212","f5ba2df35f2749ce914289ff8bdf2d19","d55484561e47489a850db5c6b48bd2ff","4bc56f5b24774e1f883756f5b9a5ce48","aac986ca1c7c4299acf88cbe55b0f442","0391f6b76e41425aa4b00f805d2083c2","15453ec906af4194b86403db07b51cca","c9f085dc64844fe5a524cc3972dfb5e2","16bca053ee00444197bc07414b565397","8d6a437d269c41978c175f063dbd4caa","041768562b684a33809e13bb03c79948","00368abc477a44a0a59f3765593b99bc","b8f58ce52e8c444394ef4c95667a4b7d","624f065b0a1c4bb4a1d1e67fcc5796cc","3d8b2a92585a4636a3bf0f822b35ff50","4d0def2a8b8a44a68d0c9487ed389178","cfbced91bce64d0a8d334588c0cad9f0","2b96c5ed05a64a08bf4a8dcbd9b4023f","8079fa25dc3e495fb3cf1fe61409e321","9e1ada9deac24d36b4871b4e8d124b0c","ea2973032ee44c498614d5bff19b4e93","04a899371cb04695a3f5d6c364bfc6ed"]},"id":"7b73ab39-ffaf-4b9e-86e5-782963c6134b","outputId":"95846603-b0ab-4c0b-f2c7-e372b81c0f25","executionInfo":{"status":"ok","timestamp":1670337129739,"user_tz":-60,"elapsed":1612694,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["    "]},{"output_type":"display_data","data":{"text/plain":["#0:   0%|          | 0/7631 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9dab51aacacd4747803af4797bc8a5be"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["#1:   0%|          | 0/7630 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"748887480fe6462a9118c0fd99dfafcd"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["    "]},{"output_type":"display_data","data":{"text/plain":["#1:   0%|          | 0/750 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"611e68f4e7ac40cf8962baf4e2217212"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["#0:   0%|          | 0/750 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"00368abc477a44a0a59f3765593b99bc"}},"metadata":{}}],"source":["common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=2)"],"id":"7b73ab39-ffaf-4b9e-86e5-782963c6134b"},{"cell_type":"markdown","metadata":{"id":"bwbvjiWNHW2r"},"source":["Now we want to save the Common Voice Dataset to the Colab VM's disk in order to zip it and move it to Google Drive where we can store it permanently. Colab doesn't keep the runtime alive forever, so we need to save the dataset to be able to use it by uploading it on a new runtime without going through the previuos steps every time."],"id":"bwbvjiWNHW2r"},{"cell_type":"code","source":["common_voice.save_to_disk(\"common_voice\")"],"metadata":{"id":"vPLi0nLChnwu"},"id":"vPLi0nLChnwu","execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","print(os.listdir(\"./common_voice/\"))\n","print(os.listdir(\"./common_voice/train\"))\n","print(os.listdir(\"./common_voice/test\"))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6iaD3jBjhrBz","executionInfo":{"status":"ok","timestamp":1670337241201,"user_tz":-60,"elapsed":292,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"}},"outputId":"8fe6c2aa-1374-4d29-8e75-a7a105c516b5"},"id":"6iaD3jBjhrBz","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['test', 'train', 'dataset_dict.json']\n","['dataset.arrow', 'state.json', 'dataset_info.json']\n","['dataset.arrow', 'state.json', 'dataset_info.json']\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4H0rl1mk_gw8","outputId":"0e7bc864-6160-4e9b-c10e-2b3b6dae9998","executionInfo":{"status":"ok","timestamp":1670337245216,"user_tz":-60,"elapsed":176,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["16099362397\n"]}],"source":["def get_dir_size(path='/content/common_voice'):\n","    total = 0\n","    with os.scandir(path) as it:\n","        for entry in it:\n","            if entry.is_file():\n","                total += entry.stat().st_size\n","            elif entry.is_dir():\n","                total += get_dir_size(entry.path)\n","    return total\n","sz = get_dir_size(path='/content/common_voice')\n","print(sz)"],"id":"4H0rl1mk_gw8"},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y2ZjufKr5Z4D","executionInfo":{"status":"ok","timestamp":1670337263055,"user_tz":-60,"elapsed":13652,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"}},"outputId":"1d6ce766-85fc-42e6-85b1-264587a06e91"},"id":"y2ZjufKr5Z4D","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["!zip -r /content/common_voice.zip /content/common_voice"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QcUSCLpkDahk","executionInfo":{"status":"ok","timestamp":1670337482042,"user_tz":-60,"elapsed":213760,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"}},"outputId":"fbbcc34d-82f0-4dda-8ba8-93187cc2c9cf"},"id":"QcUSCLpkDahk","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["  adding: content/common_voice/ (stored 0%)\n","  adding: content/common_voice/test/ (stored 0%)\n","  adding: content/common_voice/test/dataset.arrow (deflated 84%)\n","  adding: content/common_voice/test/state.json (deflated 41%)\n","  adding: content/common_voice/test/dataset_info.json (deflated 51%)\n","  adding: content/common_voice/train/ (stored 0%)\n","  adding: content/common_voice/train/dataset.arrow (deflated 85%)\n","  adding: content/common_voice/train/state.json (deflated 41%)\n","  adding: content/common_voice/train/dataset_info.json (deflated 51%)\n","  adding: content/common_voice/dataset_dict.json (stored 0%)\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"KSRAO_ex1P4_","outputId":"0598e00e-bbc4-48d1-8478-54f2bc939917","executionInfo":{"status":"ok","timestamp":1670337755107,"user_tz":-60,"elapsed":16542,"user":{"displayName":"Fine-Tune Whisper","userId":"15628753720930669248"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/drive/MyDrive/common_voice.zip'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":27}],"source":["import shutil\n","shutil.move(\"/content/common_voice.zip\", \"/content/drive/MyDrive\")"],"id":"KSRAO_ex1P4_"}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["560332eb-3558-41a1-b500-e83a9f695f84","93748af7-b917-4ecf-a0c8-7d89077ff9cb","d2ef23f3-f4a8-483a-a2dc-080a7496cb1b","8d230e6d-624c-400a-bbf5-fa660881df25","d62bb2ab-750a-45e7-82e9-61d6f4805698","daf2a825-6d9f-4a23-b145-c37c0039075b","2178dea4-80ca-47b6-b6ea-ba1915c90c06","7f404cf9-4345-468c-8196-4bd101d9bd51"],"provenance":[]},"gpuClass":"standard","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":"3.8.9"},"widgets":{"application/vnd.jupyter.widget-state+json":{"9374b2fa3ff34a828b3ff9041a7ea173":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_90698c8c395d46feac323ea2a1822109","IPY_MODEL_a03e589588b34ff4aadef5a18d0360c0","IPY_MODEL_c599caa187c341b282b4d3b90363d9ea"],"layout":"IPY_MODEL_c88707fe5d7b4637a715e7b104e230ca"}},"90698c8c395d46feac323ea2a1822109":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_19aba22aab2f42c385b8d4c6ab1eecbb","placeholder":"​","style":"IPY_MODEL_dc4081c55bcf444c9a77c6e46255d38c","value":"Downloading 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