From 00a8a8d2927b9ff32be349c735b1f4899d6b2b8f Mon Sep 17 00:00:00 2001 From: Taejin Park Date: Fri, 20 Dec 2024 16:50:40 -0800 Subject: [PATCH 1/4] Adding tutorial notebooks Signed-off-by: Taejin Park --- ...ortformer_diar_4spk-v1_callhome-part1.yaml | 12 +- .../sortformer_diar_4spk-v1_dihard3-dev.yaml | 12 +- .../End_to_end_Diarization_Inference.ipynb | 651 ++++++++++++++ .../End_to_end_Diarization_Training.ipynb | 815 ++++++++++++++++++ tutorials/speaker_tasks/images/ats.png | Bin 0 -> 334373 bytes .../speaker_tasks/images/intro_comparison.png | Bin 0 -> 325066 bytes tutorials/speaker_tasks/images/loss_types.png | Bin 0 -> 456976 bytes tutorials/speaker_tasks/images/sortformer.png | Bin 0 -> 373277 bytes 8 files changed, 1478 insertions(+), 12 deletions(-) create mode 100644 tutorials/speaker_tasks/End_to_end_Diarization_Inference.ipynb create mode 100644 tutorials/speaker_tasks/End_to_end_Diarization_Training.ipynb create mode 100644 tutorials/speaker_tasks/images/ats.png create mode 100644 tutorials/speaker_tasks/images/intro_comparison.png create mode 100644 tutorials/speaker_tasks/images/loss_types.png create mode 100644 tutorials/speaker_tasks/images/sortformer.png diff --git a/examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_callhome-part1.yaml b/examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_callhome-part1.yaml index 9b7a9701c4f2..4c9ce8f08cf0 100644 --- a/examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_callhome-part1.yaml +++ b/examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_callhome-part1.yaml @@ -5,9 +5,9 @@ # These parameters were optimized on CallHome Dataset from the NIST SRE 2000 Disc8, especially from the part1 (callhome1) specified in: Kaldi, “Kaldi x-vector recipe v2,” https://github.com/kaldi-asr/kaldi/blob/master/egs/callhome_diarization/v2/run.sh # Trial 24682 finished with value: 0.10257785779242055 and parameters: {'onset': 0.53, 'offset': 0.49, 'pad_onset': 0.23, 'pad_offset': 0.01, 'min_duration_on': 0.42, 'min_duration_off': 0.34}. Best is trial 24682 with value: 0.10257785779242055. parameters: - onset: 0.53 # Onset threshold for detecting the beginning and end of a speech - offset: 0.49 # Offset threshold for detecting the end of a speech - pad_onset: 0.23 # Adding durations before each speech segment - pad_offset: 0.01 # Adding durations after each speech segment - min_duration_on: 0.42 # Threshold for small non-speech deletion - min_duration_off: 0.34 # Threshold for short speech segment deletion \ No newline at end of file + onset: 0.53 # Onset threshold for detecting the beginning of a speech segment + offset: 0.49 # Offset threshold for detecting the end of a speech segment + pad_onset: 0.23 # Adds the specified duration at the beginning of each speech segment + pad_offset: 0.01 # Adds the specified duration at the end of each speech segment + min_duration_on: 0.42 # Removes short silences if the duration is less than the specified minimum duration + min_duration_off: 0.34 # Removes short speech segments if the duration is less than the specified minimum duration \ No newline at end of file diff --git a/examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_dihard3-dev.yaml b/examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_dihard3-dev.yaml index ebf994c10f2e..e168a97083bc 100644 --- a/examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_dihard3-dev.yaml +++ b/examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_dihard3-dev.yaml @@ -5,9 +5,9 @@ # These parameters were optimized on the development split of DIHARD3 dataset (See https://arxiv.org/pdf/2012.01477). # Trial 732 finished with value: 0.12171946949255649 and parameters: {'onset': 0.64, 'offset': 0.74, 'pad_onset': 0.06, 'pad_offset': 0.0, 'min_duration_on': 0.1, 'min_duration_off': 0.15}. Best is trial 732 with value: 0.12171946949255649. parameters: - onset: 0.64 # Onset threshold for detecting the beginning and end of a speech - offset: 0.74 # Offset threshold for detecting the end of a speech - pad_onset: 0.06 # Adding durations before each speech segment - pad_offset: 0.0 # Adding durations after each speech segment - min_duration_on: 0.1 # Threshold for small non-speech deletion - min_duration_off: 0.15 # Threshold for short speech segment deletion \ No newline at end of file + onset: 0.64 # Onset threshold for detecting the beginning of a speech segment + offset: 0.74 # Offset threshold for detecting the end of a speech segment + pad_onset: 0.06 # Adds the specified duration at the beginning of each speech segment + pad_offset: 0.0 # Adds the specified duration at the end of each speech segment + min_duration_on: 0.1 # Removes short silences if the duration is less than the specified minimum duration + min_duration_off: 0.15 # Removes short speech segments if the duration is less than the specified minimum duration \ No newline at end of file diff --git a/tutorials/speaker_tasks/End_to_end_Diarization_Inference.ipynb b/tutorials/speaker_tasks/End_to_end_Diarization_Inference.ipynb new file mode 100644 index 000000000000..60d47f2107f5 --- /dev/null +++ b/tutorials/speaker_tasks/End_to_end_Diarization_Inference.ipynb @@ -0,0 +1,651 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.\n", + "\n", + "Instructions for setting up Colab are as follows:\n", + "1. Open a new Python 3 notebook.\n", + "2. Import this notebook from GitHub (File -> Upload Notebook -> \"GITHUB\" tab -> copy/paste GitHub URL)\n", + "3. Connect to an instance with a GPU (Runtime -> Change runtime type -> select \"GPU\" for hardware accelerator)\n", + "4. Run this cell to set up dependencies.\n", + "\"\"\"\n", + "# If you're using Google Colab and not running locally, run this cell.\n", + "\n", + "## Install dependencies\n", + "!pip install wget\n", + "!apt-get install sox libsndfile1 ffmpeg\n", + "!pip install text-unidecode\n", + "!pip install ipython\n", + "\n", + "# ## Install NeMo\n", + "BRANCH = 'main'\n", + "!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[asr]\n", + "\n", + "## Install TorchAudio\n", + "!pip install torchaudio -f https://download.pytorch.org/whl/torch_stable.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# End-to-End Speaker Diarization in NeMo\n", + "\n", + "Traditional cascaded (also referred to as modular or pipelined) speaker diarization systems consists of multiple modules such as speaker activity detection (SAD) module and speaker embedding extractor moddule. Cascaded systems are often time-consuming to develop since each model should be separately trained and optimized. \n", + "\n", + "On the other hand, end-to-end speaker diarization systems pursue a much more simplified version of system where a single neural network model that accepts raw audio signal and outputs speaker activity for each audio frame. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sortformer Diarization Inference\n", + "\n", + "As explained in the [Sortformer Diarization Training](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Training.ipynb) tutorial, Sortformer is trained with Sort-Loss to generate speaker segments in arrival-time order. If a diarization model can generate speaker segments in a pre-defined rule or order, we do not need to match the permutations when we train diarization model with multi-speaker automatic speech recognition (ASR) models or we do not need to match permutations from each window when a diarization model is running in streaming mode where audio chunk sequences are processed, creating a problem of permutation matchin between inference windows. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"PIL" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A toy example for speaker diarization with a Sortformer model " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download a toy example audio file (`an4_diarize_test.wav`) and its ground-truth label file (`an4_diarize_test.rttm`)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import wget\n", + "ROOT = os.getcwd()\n", + "data_dir = os.path.join(ROOT,'data')\n", + "os.makedirs(data_dir, exist_ok=True)\n", + "an4_audio = os.path.join(data_dir,'an4_diarize_test.wav')\n", + "an4_rttm = os.path.join(data_dir,'an4_diarize_test.rttm')\n", + "if not os.path.exists(an4_audio):\n", + " an4_audio_url = \"https://nemo-public.s3.us-east-2.amazonaws.com/an4_diarize_test.wav\"\n", + " an4_audio = wget.download(an4_audio_url, data_dir)\n", + "if not os.path.exists(an4_rttm):\n", + " an4_rttm_url = \"https://nemo-public.s3.us-east-2.amazonaws.com/an4_diarize_test.rttm\"\n", + " an4_rttm = wget.download(an4_rttm_url, data_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot and listen to the audio. Pay attention to the two speakers in the audio clip." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/taejinp/anaconda3/envs/nv121824/lib/python3.10/site-packages/librosa/core/audio.py:13: UserWarning: A NumPy version >=1.23.5 and <2.3.0 is required for this version of SciPy (detected version 1.23.0)\n", + " import scipy\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import IPython\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import librosa\n", + "\n", + "sr = 16000\n", + "signal, sr = librosa.load(an4_audio,sr=sr) \n", + "\n", + "fig,ax = plt.subplots(1,1)\n", + "fig.set_figwidth(20)\n", + "fig.set_figheight(2)\n", + "plt.plot(np.arange(len(signal)),signal,'gray')\n", + "fig.suptitle('Reference merged an4 audio', fontsize=16)\n", + "plt.xlabel('time (secs)', fontsize=18)\n", + "ax.margins(x=0)\n", + "plt.ylabel('signal strength', fontsize=16)\n", + "a,_ = plt.xticks();plt.xticks(a,a/sr)\n", + "\n", + "IPython.display.Audio(an4_audio)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have downloaded the example audio file contains multiple speakers, we can feed the audio clip into Sortformer diarizer and get the speaker diarization results.\n", + "\n", + "To download Sortformer diarizer from [Sortformer HuggingFace model card](https://huggingface.co/nvidia/diar_sortformer_4spk-v1), you need to get a [HuggingFace Acces Token](https://huggingface.co/docs/hub/en/security-tokens) and feed your access token in your python environment using [HuggingFace CLI](https://huggingface.co/docs/huggingface_hub/main/en/guides/cli).\n", + "\n", + "If you are having trouble getting a HuggingFace token, you can download Sortformer model from [Sortformer HuggingFace model card](https://huggingface.co/nvidia/diar_sortformer_4spk-v1) and specify the path to the downloaded model.\n", + "\n", + "When excuting `diarize()` function, if you specify `include_tensor_outputs=True`, the diarization model will return the predicted speaker-labeled segments and tensors containing T by N (N is number of max speakers) sigmoid values for each audio frame. \n", + "\n", + "Without `include_tensor_outputs` variable or `include_tensor_outputs=False`, only speaker labeled segments will be returned." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[NeMo W 2024-12-20 16:19:49 modelPT:176] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n", + " Train config : \n", + " manifest_filepath: null\n", + " sample_rate: 16000\n", + " num_spks: 4\n", + " session_len_sec: 90\n", + " soft_label_thres: 0.5\n", + " soft_targets: false\n", + " labels: null\n", + " batch_size: 4\n", + " shuffle: true\n", + " num_workers: 18\n", + " validation_mode: false\n", + " use_lhotse: false\n", + " use_bucketing: false\n", + " num_buckets: 10\n", + " bucket_duration_bins:\n", + " - 10\n", + " - 20\n", + " - 30\n", + " - 40\n", + " - 50\n", + " - 60\n", + " - 70\n", + " - 80\n", + " - 90\n", + " pin_memory: true\n", + " min_duration: 80\n", + " max_duration: 90\n", + " batch_duration: 400\n", + " quadratic_duration: 1200\n", + " bucket_buffer_size: 20000\n", + " shuffle_buffer_size: 10000\n", + " window_stride: 0.01\n", + " subsampling_factor: 8\n", + " \n", + "[NeMo W 2024-12-20 16:19:49 modelPT:183] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n", + " Validation config : \n", + " manifest_filepath: null\n", + " is_tarred: false\n", + " tarred_audio_filepaths: null\n", + " sample_rate: 16000\n", + " num_spks: 4\n", + " session_len_sec: 90\n", + " soft_label_thres: 0.5\n", + " soft_targets: false\n", + " labels: null\n", + " batch_size: 4\n", + " shuffle: false\n", + " num_workers: 18\n", + " validation_mode: true\n", + " use_lhotse: false\n", + " use_bucketing: false\n", + " drop_last: false\n", + " pin_memory: true\n", + " window_stride: 0.01\n", + " subsampling_factor: 8\n", + " \n", + "[NeMo W 2024-12-20 16:19:49 modelPT:189] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n", + " Test config : \n", + " manifest_filepath: null\n", + " is_tarred: false\n", + " tarred_audio_filepaths: null\n", + " sample_rate: 16000\n", + " num_spks: 4\n", + " session_len_sec: 90\n", + " soft_label_thres: 0.5\n", + " soft_targets: false\n", + " labels: null\n", + " batch_size: 4\n", + " shuffle: false\n", + " seq_eval_mode: true\n", + " num_workers: 18\n", + " validation_mode: true\n", + " use_lhotse: false\n", + " use_bucketing: false\n", + " drop_last: false\n", + " pin_memory: true\n", + " window_stride: 0.01\n", + " subsampling_factor: 8\n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[NeMo I 2024-12-20 16:19:49 features:305] PADDING: 16\n", + "[NeMo I 2024-12-20 16:19:51 save_restore_connector:275] Model SortformerEncLabelModel was successfully restored from /home/taejinp/.cache/huggingface/hub/models--nvidia--diar_sortformer_4spk-v1/snapshots/992636d4552cc94fb80acfce378793bfd32dd9bd/diar_sortformer_4spk-v1.nemo.\n", + "[NeMo I 2024-12-20 16:19:51 vad_utils:81] No postprocessing YAML file has been provided. Default postprocessing configurations will be applied.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Diarizing: 100%|██████████| 1/1 [00:00<00:00, 12.66it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['0.400 2.880 speaker_0', '3.280 5.270 speaker_1']\n", + "torch.Size([1, 66, 4])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from nemo.collections.asr.models import SortformerEncLabelModel\n", + "from huggingface_hub import get_token as get_hf_token\n", + "import torch\n", + "\n", + "if get_hf_token() is not None and get_hf_token().startswith(\"hf_\"):\n", + " # If you have logged into HuggingFace hub and have access token \n", + " diar_model = SortformerEncLabelModel.from_pretrained(\"nvidia/diar_sortformer_4spk-v1\")\n", + "else:\n", + " # You can downloaded \".nemo\" file from https://huggingface.co/nvidia/diar_sortformer_4spk-v1 and specify the path.\n", + " diar_model = SortformerEncLabelModel.restore_from(restore_path=\"/path/to/diar_sortformer_4spk-v1.nemo\", map_location=torch.device('cuda'), strict=False)\n", + "\n", + "pred_list, pred_tensor_list = diar_model.diarize(audio=an4_audio, include_tensor_outputs=True)\n", + "\n", + "print(pred_list[0])\n", + "print(pred_tensor_list[0].shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now let's visualize the predicted speaker diarization results. The diarization model outputs a tensor where each row represents a speaker and each column represents a frame. The sigmoid values in the tensor represent the probability of the frame being spoken by that speaker.\n", + "\n", + "In the following code, we'll plot the predicted speaker diarization results for the sample audio file." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "def plot_diarout(preds, sample_idx):\n", + " preds_mat = preds[sample_idx].cpu().numpy().transpose()\n", + " cmap_str, grid_color_p= 'viridis', 'gray'\n", + " LW, FS = 0.4, 36\n", + "\n", + " yticklabels = [\"spk0\", \"spk1\", \"spk2\", \"spk3\"]\n", + " yticks = np.arange(len(yticklabels))\n", + " fig, axs = plt.subplots(1, 1, figsize=(30, 3)) # 1 row, 2 columns for preds and targets\n", + "\n", + " axs.imshow(preds_mat, cmap=cmap_str, interpolation='nearest') #, aspect=aspect_float)\n", + " axs.set_title('Predictions', fontsize=FS)\n", + " axs.set_xticks(np.arange(-.5, preds_mat.shape[1], 1), minor=True)\n", + " axs.set_yticks(yticks)\n", + " axs.set_yticklabels(yticklabels)\n", + " axs.set_xlabel(f\"80 ms Frames\", fontsize=FS)\n", + " axs.grid(which='minor', color=grid_color_p, linestyle='-', linewidth=LW)\n", + "\n", + " plt.savefig('plot.png', dpi=300) # bbox_inches='tight')\n", + " plt.show()\n", + "\n", + "\n", + "plot_diarout(pred_tensor_list[0], sample_idx=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the first speaker always gets the first generic speaker label `spk0`. Sortformer model is trained to generate speaker labels in arrival time order, thus permutations of speakers are always predictable if we know the arrival time order of speakers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Post-Processing of Speaker Timestamps\n", + "\n", + "In the previous steps, we have obtained predictions of speaker activity in a form of Tensors. While the floating point probability values can be interpreted as speaker probabilities, these values are not designed to consumed as is and still requires to be processed to be segment information which has start and end time for each time stamp.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from omegaconf import OmegaConf\n", + "import os\n", + "import wget\n", + "import pandas as pd\n", + "from nemo.collections.asr.parts.utils.speaker_utils import rttm_to_labels, labels_to_pyannote_object\n", + "from pyannote.metrics.diarization import DiarizationErrorRate\n", + "\n", + "ROOT = os.getcwd()\n", + "data_dir = os.path.join(ROOT,'data')\n", + "\n", + "# MODEL_CONFIG = os.path.join(data_dir,'sortformer_diar_4spk-v1_callhome-part1.yaml')\n", + "yaml_name=\"sortformer_diar_4spk-v1_dihard3-dev.yaml\"\n", + "MODEL_CONFIG = os.path.join(data_dir, yaml_name)\n", + "# if not os.path.exists(MODEL_CONFIG):\n", + "if True:\n", + " config_url = f\"https://raw.githubusercontent.com/NVIDIA/NeMo/main/examples/speaker_tasks/diarization/conf/post_processing/{yaml_name}\"\n", + " MODEL_CONFIG = wget.download(config_url, data_dir)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The post-processing yaml file `sortformer_diar_4spk-v1_dihard3-dev.yaml` is containing parameters that are optimized to have the lowest Diarization Error Rate (DER) on the [DiHARD 3 development set](https://catalog.ldc.upenn.edu/LDC2022S12)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[NeMo I 2024-12-20 15:55:33 vad_utils:89] Postprocessing YAML file '/home/taejinp/projects/sortformer_docs/NeMo/tutorials/speaker_tasks/data/sortformer_diar_4spk-v1_dihard3-dev (1).yaml' has been loaded.\n", + "{\n", + " \"onset\": 0.64,\n", + " \"offset\": 0.74,\n", + " \"pad_onset\": 0.06,\n", + " \"pad_offset\": 0.0,\n", + " \"min_duration_on\": 0.1,\n", + " \"min_duration_off\": 0.15\n", + "}\n" + ] + } + ], + "source": [ + "from nemo.collections.asr.parts.utils.vad_utils import load_postprocessing_from_yaml\n", + "import json\n", + "from omegaconf import OmegaConf \n", + "post_processing_params = load_postprocessing_from_yaml(MODEL_CONFIG)\n", + "print(json.dumps(OmegaConf.to_container(post_processing_params), indent=4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters in post-processing yaml configurations perform the following tasks:\n", + "\n", + "| **Parameter** | **Description** |\n", + "|-------------------:|--------------------------------------------------------------|\n", + "| **onset** | Onset threshold for detecting the beginning of a speech segment. |\n", + "| **offset** | Offset threshold for detecting the end of a speech segment. |\n", + "| **pad_onset** | Adds the specified duration at the beginning of each speech segment. |\n", + "| **pad_offset** | Adds the specified duration at the end of each speech segment. |\n", + "| **min_duration_on**| Removes short silences if the duration is less than the specified minimum duration. |\n", + "| **min_duration_off**| Removes short speech segments if the duration is less than the specified minimum duration. |\n", + "\n", + "Now let's check the diarization output timestamps and compare how post-processing changes the diarization output." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[NeMo I 2024-12-20 15:59:24 vad_utils:81] No postprocessing YAML file has been provided. Default postprocessing configurations will be applied.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Diarizing: 100%|██████████| 1/1 [00:00<00:00, 11.11it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [Default Binarized Diarization Output]: \n", + " Start Time End Time Speaker\n", + "0 0.400 2.880 speaker_0\n", + "1 3.280 5.270 speaker_1\n", + "[NeMo I 2024-12-20 15:59:24 vad_utils:89] Postprocessing YAML file '/home/taejinp/projects/sortformer_docs/NeMo/tutorials/speaker_tasks/data/sortformer_diar_4spk-v1_dihard3-dev (1).yaml' has been loaded.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Diarizing: 100%|██████████| 1/1 [00:00<00:00, 12.67it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [Post-processed Diarization Output]: \n", + " Start Time End Time Speaker\n", + "0 0.340 2.800 speaker_0\n", + "1 3.220 5.270 speaker_1\n", + " [Ground-truth Diarization Output]: \n", + " Start Time End Time Speaker\n", + "0 0.299 2.77 A\n", + "1 3.164 5.147 B\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "\n", + "def show_diar_df(pred_session_list):\n", + " data = [segment.split() for segment in pred_session_list]\n", + " df = pd.DataFrame(data, columns=['Start Time', 'End Time', 'Speaker'])\n", + " print(df)\n", + "\n", + "# Call `diarize` method without postprocessing params\n", + "pred_list_bn = diar_model.diarize(audio=an4_audio)\n", + "# \"data/input_manifest.json\")\n", + "print(f\" [Default Binarized Diarization Output]: \")\n", + "show_diar_df(pred_list_bn[0])\n", + "\n", + "# Call `diarize` method with postprocessing params\n", + "pred_list_pp = diar_model.diarize(audio=an4_audio, postprocessing_yaml=MODEL_CONFIG)\n", + "print(f\" [Post-processed Diarization Output]: \")\n", + "show_diar_df(pred_list_pp[0])\n", + "\n", + "print(f\" [Ground-truth Diarization Output]: \")\n", + "ref_labels = rttm_to_labels(an4_rttm)\n", + "show_diar_df(ref_labels)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that the post-processed segments have more on-set padding while the differences are not significant. \n", + "\n", + "Now let's calculate DER (Diarization Error Rate) between the predicted labels and the ground-truth labels for both raw binarized and post-processed diarization outputs." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[NeMo W 2024-12-20 15:55:34 nemo_logging:361] /home/taejinp/anaconda3/envs/nv121824/lib/python3.10/site-packages/pyannote/metrics/utils.py:200: UserWarning: 'uem' was approximated by the union of 'reference' and 'hypothesis' extents.\n", + " warnings.warn(\n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Raw Binarization DER: 0.101033\n", + "Post-Processing DER: 0.056129\n" + ] + } + ], + "source": [ + "# Get the refernce labels from ground-truth RTTM file\n", + "ref_labels = rttm_to_labels(an4_rttm)\n", + "\n", + "reference = labels_to_pyannote_object(ref_labels, uniq_name=\"binarize\")\n", + "hypothesis1 = labels_to_pyannote_object(pred_list_bn[0], uniq_name=\"binarize\")\n", + "der_metric1 = DiarizationErrorRate(collar=0, skip_overlap=False)\n", + "der_metric1(reference, hypothesis1, detailed=True)\n", + "print(f\"Raw Binarization DER: {abs(der_metric1):.6f}\")\n", + "\n", + "reference = labels_to_pyannote_object(ref_labels, uniq_name=\"post_processing\")\n", + "hypothesis2 = labels_to_pyannote_object(pred_list_pp[0], uniq_name=\"post_processing\")\n", + "der_metric2 = DiarizationErrorRate(collar=0, skip_overlap=False)\n", + "der_metric2(reference, hypothesis2, detailed=True)\n", + "print(f\"Post-Processing DER: {abs(der_metric2):.6f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the diarization output with post-processing is optimized to get the lowest DER for the given sigmoid tensor outputs, it generaly gives lower DER values when compared to the raw binarized diarization output. To achieve the lowest DER, it is recommended to use the post-processing parameters that are optimized for your dataset of interest and your diarization model. " + ] + } + ], + "metadata": { + "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": "3.10.12" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + }, + "vscode": { + "interpreter": { + "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/speaker_tasks/End_to_end_Diarization_Training.ipynb b/tutorials/speaker_tasks/End_to_end_Diarization_Training.ipynb new file mode 100644 index 000000000000..df8143bda061 --- /dev/null +++ b/tutorials/speaker_tasks/End_to_end_Diarization_Training.ipynb @@ -0,0 +1,815 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.\n", + "\n", + "Instructions for setting up Colab are as follows:\n", + "1. Open a new Python 3 notebook.\n", + "2. Import this notebook from GitHub (File -> Upload Notebook -> \"GITHUB\" tab -> copy/paste GitHub URL)\n", + "3. Connect to an instance with a GPU (Runtime -> Change runtime type -> select \"GPU\" for hardware accelerator)\n", + "4. Run this cell to set up dependencies.\n", + "5. Restart the runtime (Runtime -> Restart Runtime) for any upgraded packages to take effect\n", + "\"\"\"\n", + "\n", + "NEMO_DIR_PATH = \"NeMo\"\n", + "BRANCH = 'main'\n", + "\n", + "! git clone https://github.com/NVIDIA/NeMo\n", + "%cd NeMo\n", + "! python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n", + "%cd .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# End-to-end Speaker Diarization with Sortformer\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Diarizer in Speaker Diarization Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Input: Clustering as Initialization\n", + "\n", + "MSDD model is a diarizer model that accepts two different data inputs:\n", + "\n", + " 1. Cluster-average embeddings\n", + " 2. Multi-scale embedding sequence \n", + " \n", + "The two input signals are depicted in the following figure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"MSDD_inputs\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By initializing the diarization task with a clustering algorithm, we can estimate the number of speakers and cluster-average embeddings. Thus, the cluster-average embeddings provide the speaker profile of each speaker. The cluster-average embeddings we provide can be regarded as reference signals for providing seed speaker profiles.\n", + "\n", + "Once we obtain the fixed (or estimated) number of speakers, the speaker diarization problem becomes a binary classification task where we need to estimate whether a certain speaker's speech exists or not at a given timestep. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example Data Creation\n", + "\n", + "- Please skip this section and go directly to [Prepare Training data for MSDD](#Prepare-Training-data-for-MSDD) section if you have your own speaker diarization dataset. \n", + "\n", + "In this tutorial, we use [NeMo Multispeaker Simulator](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/Multispeaker_Simulator.ipynb) and the Librispeech corpus to generate a toy training dataset for demonstration purpose. You can replace the simulated dataset with your own datasets if you have proper speaker annotations (RTTM files) for the dataset. If you do not have access to any speaker diarization datasets, you can use [NeMo Multispeaker Simulator](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/Multispeaker_Simulator.ipynb) by generating a good amount of data samples to meet your needs. \n", + "\n", + "For more details regarding data simulator, please follow the descriptions in [NeMo Multispeaker Simulator](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/Multispeaker_Simulator.ipynb) and we will not cover configurations and detailed process of data simulation in this tutorial. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install dependencies for data simulator\n", + "!apt-get install sox libsndfile1 ffmpeg\n", + "!pip install wget\n", + "!pip install unidecode\n", + "!pip install \"matplotlib>=3.3.2\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data Simulation Step 1: Download Required Resources\n", + "\n", + "We need to download the LibriSpeech corpus and corresponding word alignments for generating synthetic multi-speaker audio sessions. In addition, we need to download necessary data cleaning scripts from NeMo git." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "NEMO_DIR_PATH = \"NeMo\"\n", + "BRANCH = 'main'\n", + "\n", + "# download scripts if not already there \n", + "if not os.path.exists('NeMo/scripts'):\n", + " print(\"Downloading necessary scripts\")\n", + " !mkdir -p NeMo/scripts/dataset_processing\n", + " !mkdir -p NeMo/scripts/speaker_tasks\n", + " !wget -P NeMo/scripts/dataset_processing/ https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/dataset_processing/get_librispeech_data.py\n", + " !wget -P NeMo/scripts/speaker_tasks/ https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/speaker_tasks/create_alignment_manifest.py\n", + " !wget -P NeMo/scripts/speaker_tasks/ https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/speaker_tasks/create_msdd_train_dataset.py \n", + " !wget -P NeMo/scripts/speaker_tasks/ https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/speaker_tasks/pathfiles_to_diarize_manifest.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have downloaded all the necessary scripts for data creation and preparation, we can start the data simulation step by downloading the LibriSpeech corpus." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p LibriSpeech\n", + "!python {NEMO_DIR_PATH}/scripts/dataset_processing/get_librispeech_data.py \\\n", + " --data_root LibriSpeech \\\n", + " --data_sets dev_clean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can get the forced word alignments data for the LibriSpeech corpus from [this repository.](https://github.com/CorentinJ/librispeech-alignments). Full forced alignments data can be downloaded at [google drive link for alignments data](https://drive.google.com/file/d/1WYfgr31T-PPwMcxuAq09XZfHQO5Mw8fE/view?usp=sharing). We will download only a subset of forced alignment data containing dev-clean part." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!wget -nc https://dldata-public.s3.us-east-2.amazonaws.com/LibriSpeech_Alignments.tar.gz\n", + "!tar -xzf LibriSpeech_Alignments.tar.gz\n", + "!rm -f LibriSpeech_Alignments.tar.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data Simulation Step 2: Produce Manifest File with Forced Alignments\n", + "\n", + "We will merge the LibriSpeech manifest files and LibriSpeech forced alignments into one manifest file for ease of use when generating synthetic data. Create alignment files by running the following script.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python NeMo/scripts/speaker_tasks/create_alignment_manifest.py \\\n", + " --input_manifest_filepath LibriSpeech/dev_clean.json \\\n", + " --base_alignment_path LibriSpeech_Alignments \\\n", + " --output_manifest_filepath ./dev-clean-align.json \\\n", + " --ctm_output_directory ./ctm_out \\\n", + " --libri_dataset_split dev-clean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data Simulation Step 3: Set data simulation parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have downloaded all the sources we need for data creation, we need to download data simulator configurations in `.yaml` format. Download the YAML file and download `data_simulator.py` script from NeMo repository." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from omegaconf import OmegaConf\n", + "import os\n", + "ROOT = os.getcwd()\n", + "conf_dir = os.path.join(ROOT,'conf')\n", + "!mkdir -p $conf_dir\n", + "CONFIG_PATH = os.path.join(conf_dir, 'data_simulator.yaml')\n", + "if not os.path.exists(CONFIG_PATH):\n", + " !wget -P $conf_dir https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/tools/speech_data_simulator/conf/data_simulator.yaml\n", + "\n", + "config = OmegaConf.load(CONFIG_PATH)\n", + "print(OmegaConf.to_yaml(config))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data Simulation Step 4: Generate Simulated Audio Session\n", + "\n", + "We will generate a set of example sessions with the following specifications:\n", + "\n", + "- 3 example sessions for train \n", + "- 3 example sessions for validation\n", + "- 2-speakers in each session\n", + "- 60 seconds of recordings\n", + "\n", + "We need to setup different seed for train and validation sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from nemo.collections.asr.data.data_simulation import MultiSpeakerSimulator\n", + "\n", + "# Generate train set \n", + "ROOT = os.getcwd()\n", + "data_dir = os.path.join(ROOT,'simulated_train')\n", + "config.data_simulator.random_seed=10\n", + "config.data_simulator.manifest_filepath=\"./dev-clean-align.json\"\n", + "config.data_simulator.outputs.output_dir=data_dir\n", + "config.data_simulator.session_config.num_sessions=3\n", + "config.data_simulator.session_config.num_speakers=2\n", + "config.data_simulator.session_config.session_length=60\n", + "config.data_simulator.background_noise.add_bg=False \n", + "\n", + "lg = MultiSpeakerSimulator(cfg=config)\n", + "lg.generate_sessions()\n", + "\n", + "# Generate validation set \n", + "data_dir = os.path.join(ROOT,'simulated_valid')\n", + "config.data_simulator.random_seed=20\n", + "config.data_simulator.outputs.output_dir=data_dir\n", + "\n", + "lg = MultiSpeakerSimulator(cfg=config)\n", + "lg.generate_sessions()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that parameter setting is done, generate the samples by launching `generate_sessions()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lg = MultiSpeakerSimulator(cfg=config)\n", + "lg.generate_sessions()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data preparation step 5: Listen to and Visualize Session\n", + "\n", + "Listen to the audio and visualize the corresponding speaker timestamps (recorded in a RTTM file for each session)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import os\n", + "import wget\n", + "import IPython\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import librosa\n", + "from nemo.collections.asr.parts.utils.speaker_utils import rttm_to_labels, labels_to_pyannote_object\n", + "\n", + "ROOT = os.getcwd()\n", + "data_dir = os.path.join(ROOT,'simulated_train')\n", + "audio = os.path.join(data_dir,'multispeaker_session_0.wav')\n", + "rttm = os.path.join(data_dir,'multispeaker_session_0.rttm')\n", + "\n", + "sr = 16000\n", + "signal, sr = librosa.load(audio,sr=sr) \n", + "\n", + "fig,ax = plt.subplots(1,1)\n", + "fig.set_figwidth(20)\n", + "fig.set_figheight(2)\n", + "plt.plot(np.arange(len(signal)),signal,'gray')\n", + "fig.suptitle('Synthetic Audio Session', fontsize=16)\n", + "plt.xlabel('Time (s)', fontsize=18)\n", + "plt.yticks([], [])\n", + "ax.margins(x=0)\n", + "a,_ = plt.xticks()\n", + "plt.xticks(a[:-1],a[:-1]/sr);\n", + "IPython.display.Audio(audio)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visually check the ground-truth file of the first sample by running the following commands. We can see that it has plenty of overlap between two speakers. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# display speaker labels for reference\n", + "labels = rttm_to_labels(rttm)\n", + "reference = labels_to_pyannote_object(labels)\n", + "reference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check that corresponding RTTM files are generated as ground-truth labels for training and evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!cat simulated_train/multispeaker_session_0.rttm | head -10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data preparation step 6: Check out the created files\n", + "\n", + "The following files are generated from data simulator:\n", + "\n", + "* _wav files_ (one per audio session) - the output audio sessions\n", + "* _rttm files_ (one per audio session) - the speaker timestamps for the corresponding audio session (used for diarization training)\n", + "* _list files_ (one per file type per batch of sessions) - a list of generated files of the given type (e.g., wav, rttm), used primarily for manifest creation\n", + "\n", + "Check if the files we need are generated by running the following commands." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\n Training audio files:\")\n", + "!ls simulated_train/*.wav\n", + "print(\"\\n Training audio files:\")\n", + "!ls simulated_train/*.rttm\n", + "print(\"\\n Training RTTM list content:\")\n", + "!cat simulated_train/synthetic_wav.list\n", + "print(\"\\n Training RTTM list content:\")\n", + "!cat simulated_train/synthetic_rttm.list\n", + "\n", + "print(\"\\n Validation audio files:\")\n", + "!ls simulated_valid/*.wav\n", + "print(\"\\n Validation audio files:\")\n", + "!ls simulated_valid/*.rttm\n", + "print(\"\\n Validation RTTM list content:\")\n", + "!cat simulated_valid/synthetic_wav.list\n", + "print(\"\\n Validation RTTM list content:\")\n", + "!cat simulated_valid/synthetic_rttm.list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare Training Data for MSDD" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have datasets for both train and validation (dev), we can start preparing and cleaning the data samples for training. Make sure you have the following list of files:\n", + "\n", + "**Training set** \n", + "\n", + "- Train audio files `.wav`\n", + "- A train audio list file `.list`\n", + "- Train RTTM files `.rttm`\n", + "- A train RTTM list content `.list`\n", + "\n", + "**Validation set** \n", + "\n", + "- Validation audio files `.wav`\n", + "- A validation audio list file `.list`\n", + "- Validation RTTM files `.rttm`\n", + "- A validation RTTM list file `.list`\n", + "\n", + "\n", + "Based on these files, we need to create manifest files containing data samples we have. If you don't have a `.list` file, you need to create a `.list` file for the `.wav` files and `.rttm` files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create a NeMo manifest (.json) file for training dataset\n", + "!python NeMo/scripts/speaker_tasks/pathfiles_to_diarize_manifest.py \\\n", + " --paths2audio_files='simulated_train/synthetic_wav.list' \\\n", + " --paths2rttm_files='simulated_train/synthetic_rttm.list' \\\n", + " --manifest_filepath='simulated_train/msdd_data.json'\n", + "\n", + "# create a NeMo manifest (.json) file for validation (dev) dataset\n", + "!python NeMo/scripts/speaker_tasks/pathfiles_to_diarize_manifest.py \\\n", + " --paths2audio_files='simulated_valid/synthetic_wav.list' \\\n", + " --paths2rttm_files='simulated_valid/synthetic_rttm.list' \\\n", + " --manifest_filepath='simulated_valid/msdd_data.json'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you print the content of the created manifest file, you can see that `.rttm` files in the list and `.wav` files are grouped together in the generated manifest files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\n An example line in training dataset manifest file:\")\n", + "!cat simulated_train/msdd_data.json | head -1\n", + "print(\"\\n An example line in validation Dataset manifest file:\")\n", + "!cat simulated_valid/msdd_data.json | head -1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have input a standard manifest file, we need to break down each audio clip into short audio clips so that we can put several samples in a batch. \n", + "\n", + "Before we generate a manifest file and RTTM files for training MSDD, you have to determine:\n", + "\n", + "- `window`: the window length of the base scale (the shortest scale)\n", + "- `shift`: the hop-length of the base scale (the shortest scale)\n", + "- `step_count`: how many decision steps in one data sample\n", + "\n", + "Note that these numbers should match the parameters in the configurations for your desired MSDD model. If you want to train with new parameters (`window`, `shift` and `step_count`), you need to make new manifest files with the new parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create a manifest (.json) file for training dataset\n", + "!python NeMo/scripts/speaker_tasks/create_msdd_train_dataset.py \\\n", + " --input_manifest_path='simulated_train/msdd_data.json' \\\n", + " --output_manifest_path='simulated_train/msdd_data.50step.json' \\\n", + " --pairwise_rttm_output_folder='simulated_train/' \\\n", + " --window 0.5 \\\n", + " --shift 0.25 \\\n", + " --step_count 50 \n", + " \n", + "# create a manifest (.json) file for validation (dev) dataset\n", + "!python NeMo/scripts/speaker_tasks/create_msdd_train_dataset.py \\\n", + " --input_manifest_path='simulated_valid/msdd_data.json' \\\n", + " --output_manifest_path='simulated_valid/msdd_data.50step.json' \\\n", + " --pairwise_rttm_output_folder='simulated_valid/' \\\n", + " --window 0.5 \\\n", + " --shift 0.25 \\\n", + " --step_count 50 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we broke down the training and validation dataset into 50-step samples, let's checkout how the output manifest files look like. We used 0.25 second of shift length so in theory, if there is no silence or pause in the data, the length of data sample should be `step_count*shift` which is `50*0.25=12.5` second in the example we used. However, since there are pauses between the segments in practice, the final lengths of data samples are longer than 12.5 second." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\nTraining Dataset:\")\n", + "!cat simulated_train/msdd_data.50step.json | tail -5\n", + "print(\"\\nValidation Dataset:\")\n", + "!cat simulated_valid/msdd_data.50step.json | tail -5 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train an MSDD Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have prepared all the necessary dataset, we can train an MSDD model on the prepared dataset. Download YAML file for training form NeMo repository and load the configuration into `config` variable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nemo\n", + "import nemo.collections.asr as nemo_asr\n", + "from omegaconf import OmegaConf\n", + "\n", + "NEMO_ROOT = os.getcwd()\n", + "!mkdir conf \n", + "!wget -P conf https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/speaker_tasks/diarization/conf/neural_diarizer/msdd_5scl_15_05_50Povl_256x3x32x2.yaml\n", + "MODEL_CONFIG = os.path.join(NEMO_ROOT,'conf/msdd_5scl_15_05_50Povl_256x3x32x2.yaml')\n", + "config = OmegaConf.load(MODEL_CONFIG)\n", + "print(OmegaConf.to_yaml(config))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setup the `manifest_filepath` for `train_ds` and `validation_ds` by feeding the `json` file paths from `create_msdd_train_dataset.py`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config.model.train_ds.manifest_filepath = 'simulated_train/msdd_data.50step.json'\n", + "config.model.validation_ds.manifest_filepath = 'simulated_valid/msdd_data.50step.json'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train MSDD with frozen speaker embedding model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Provide a batch size number for training in `config.model.batch_size`. In a batch, we will assign the given number of split samples (in this example, 50 step-size). Note that you might need to change this batch size if the following batch size maxes out your GPU memory size. \n", + "\n", + "`config.model.emb_batch_size` determines the number of embedding vectors attached to a computational graph. This means that \n", + "If you want to freeze the speaker embedding extractor, you should set `emb_batch_size=0`.\n", + "If you want to jointly optimize speaker embedding extractor, you need to assign an adequate number that does not max out the GPU memory. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config.batch_size=5\n", + "config.model.emb_batch_size=0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Provide paths to the temporary folders for saving timestamp data during training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config.model.train_ds.emb_dir=\"simulated_train\" \n", + "config.model.validation_ds.emb_dir=\"simulated_valid\" " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setup a speaker embedding model that will be used for speaker embedding extraction. We will use `titanet_large` model checkpoint from NGC. Note that this speaker embedding model will be saved together in a `.ckpt` file whenever pytorch lightning trainer saves checkpoint. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config.model.diarizer.speaker_embeddings.model_path=\"titanet_large\"\n", + "config.trainer.max_epochs = 5\n", + "config.trainer.strategy = 'auto'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use `pytorch_lightning` and train a model instance from class `EncDecDiarLabelModel`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pytorch_lightning as pl\n", + "from nemo.collections.asr.models import EncDecDiarLabelModel\n", + "from nemo.utils.exp_manager import exp_manager\n", + "\n", + "trainer = pl.Trainer(**config.trainer)\n", + "exp_manager(trainer, config.get(\"exp_manager\", None))\n", + "msdd_model = EncDecDiarLabelModel(cfg=config.model, trainer=trainer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we start training, let's check a few of the weights in the speaker embedding model in `msdd_model.msdd._speaker_model`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "msdd_model.msdd._speaker_model.state_dict()[\"encoder.encoder.0.mconv.0.conv.weight\"][0,:,:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you successfully ran the previous step, now it is ready to initiate a training session of MSDD. Launch `trainer.fit()` function for `msdd_model`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.fit(msdd_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this way, you can train an MSDD model and use `.ckpt` files saved during training process." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the weights in speaker embedding model again and see if the numbers are changed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "msdd_model.msdd._speaker_model.state_dict()[\"encoder.encoder.0.mconv.0.conv.weight\"][0,:,:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train MSDD and speaker embedding model together" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this time, let's attach a few speaker embeddings to a graph and jointly train the speaker embedding mode." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config.model.emb_batch_size = 100 # choose the largest number that does not max out GPU memory.\n", + "config.trainer.max_epochs = 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setup another trainer and initiate a training session with the new parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer = pl.Trainer(**config.trainer)\n", + "exp_manager(trainer, config.get(\"exp_manager\", None))\n", + "msdd_model = EncDecDiarLabelModel(cfg=config.model, trainer=trainer)\n", + "trainer.fit(msdd_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check whether there is a change in the weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + 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