"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "shifted_diarization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "utIc_ybRL3I_"
+ },
+ "source": [
+ "Finally, compute the DER and its components:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 175
+ },
+ "id": "J9AaI0oxMBrz",
+ "outputId": "e080a19e-5f67-4453-a494-361945573c5f"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'confusion': 3.531999999999698,\n",
+ " 'total': 69.32000000000005,\n",
+ " 'correct': 62.87199999999996,\n",
+ " 'missed detection': 2.9160000000003947,\n",
+ " 'false alarm': 1.9320000000000164,\n",
+ " 'diarization error rate': 0.12088863242931482}"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "metric(annotations, shifted_diarization, detailed=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "h7wu0sh6T2s2"
+ },
+ "source": [
+ "## Speech transcription of the separated source\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J9ZG3Y27MOkv"
+ },
+ "source": [
+ "We now switch to the evaluation of the speech separation part through the spectrum of its impact on automatic speech recognition.\n",
+ "\n",
+ "Here, we apply [Whisper](https://github.com/openai/whisper) pretrained speech-to-text model to each separated source. \n",
+ "The final transcription is evaluated by comparison with the reference (manual) transcriptionusing [Concatenated minimum-Permutation Word Error Rate](https://arxiv.org/abs/2004.09249) (cpWER), available in the [MeetEval](https://github.com/fgnt/meeteval) toolkit."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cvSiXpwlOMYX"
+ },
+ "source": [
+ "We start by instantiating the pipeline with hyper-parameters optimized for the speech-to-text task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "y-XE8VwhgFit",
+ "outputId": "78188c54-8ae1-491b-a8c0-2fd583d2b13a"
+ },
+ "outputs": [],
+ "source": [
+ "pipeline.instantiate(\n",
+ " {\n",
+ " \"segmentation\": {\"min_duration_off\": 0.0, \"threshold\": 0.82},\n",
+ " \"clustering\": {\n",
+ " \"method\": \"centroid\",\n",
+ " \"min_cluster_size\": 15,\n",
+ " \"threshold\": 0.68,\n",
+ " },\n",
+ " \"separation\": {\n",
+ " \"leakage_removal\": True,\n",
+ " \"asr_collar\": 0.32,\n",
+ " }\n",
+ " }\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MZCdoSTLO57A"
+ },
+ "source": [
+ "Reference transcription is loaded and normalized with Whisper built-in normalization function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jVAXMeDvOpCv"
+ },
+ "outputs": [],
+ "source": [
+ "import whisperx\n",
+ "from whisper.normalizers import EnglishTextNormalizer\n",
+ "\n",
+ "normalizer = EnglishTextNormalizer()\n",
+ "\n",
+ "references = [\n",
+ " \"So simplification of symbols you could think of. Mm-hmm. Menu, alright. Uh uh Right, I was thinking on the same lines you, instead of having too many b buttons and make it complicated for the user, may h maybe have an L_C_D_ di display or something like that, like a mobile, yeah and with menus. And if it's s somewhat similar to what you have on mobile phone, people might find it easier to browse\",\n",
+ " \"button or something like that. Yeah. Um. When they're when you've got the main things on the front of it and a section opens up or something to the other functions where you can do sound or options kind of recording, things like that inside it. 'Cause it doesn't make when you pick it up it doesn't make it really complicated to look at, it's obvious what you're doing, um.\",\n",
+ " \"Mm. Yeah.\",\n",
+ " \". And symbols that you don't necessarily understand, symbols you're meant to understand that you don't. Oh yeah. Mm-hmm. Mm. Mm-hmm. Actually that just raises a point, I wonder what our design people think, but you know on a mobile phone, you can press a key and it gives you a menu, it's got a menu display, I wonder if incorporating that into the design of a remote control might be useful, so you've got a little L_C_D_ display. With menus, yeah, yeah.\",\n",
+ "]\n",
+ "\n",
+ "references_formatted = []\n",
+ "for i in range(len(references)):\n",
+ " if references[i] != \"\":\n",
+ " references_formatted.append(normalizer(references[i]))\n",
+ "\n",
+ "references = references_formatted"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "94yALajRPdS7"
+ },
+ "source": [
+ "Apply the pipeline on the mixture to get separated sources."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 102,
+ "referenced_widgets": [
+ "cb998feff752458995c1d564d4fef3f2",
+ "09eff7052a5e43229c116e1432a706c4"
+ ]
+ },
+ "id": "1-fx2mbAPdBX",
+ "outputId": "f2cae346-b747-43d3-8c62-7e99c062c08d"
+ },
+ "outputs": [],
+ "source": [
+ "from pyannote.audio.pipelines.utils.hook import ProgressHook\n",
+ "\n",
+ "with ProgressHook() as hook:\n",
+ " diarization, sources = pipeline({\"waveform\": mixture, \"sample_rate\": sample_rate}, hook=hook)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hgkVrUVWEUzX"
+ },
+ "source": [
+ "Listen to one of the separated source:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 61
+ },
+ "id": "BSBFlQkCtWTo",
+ "outputId": "c8196394-0eb4-44b0-a03f-b84f8b0ed710"
+ },
+ "outputs": [],
+ "source": [
+ "from IPython.display import Audio, display\n",
+ "\n",
+ "source_1 = sources[:, 1]\n",
+ "display(Audio(source_1, rate=sample_rate))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-rDAcP6pPuhw"
+ },
+ "source": [
+ "Get transcription for each predicted source by applying `Whisper`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 234,
+ "referenced_widgets": [
+ "3019ebb78e664cfaba0b59916281b8b5",
+ "b1255bd58957449a8058d767545e5107",
+ "226aa8a0e909453c859ac2a54d6f60a5",
+ "ce35798f9da44ede822b630e3dd6479e",
+ "af33a938bc1d4468bfd2688f883d5039",
+ "1c32657deab94066b49b9cdd9eaa154b",
+ "5c9068a6baa4447fbbde95fbe273e216",
+ "da708345954047dfb12a7b436c09873d",
+ "1f3bc8e07e5c4136acfb5ba7e9a1ddb3",
+ "c18e7ca8c42349938c01fac2eb54dbce",
+ "46da1d6ac681473786917f10df3d60c1",
+ "215a623bd8024f4da99944ee8b410860",
+ "5f590a1431f14dcdbd6fd7f08e537dd8",
+ "3cb74cfcfc9c4d4597ce9764be019563",
+ "12be8f2cda5c4ecabf3e2989555001e5",
+ "5f8932c839b54b6ca010d7d70f78a2c6",
+ "c7ecf3bb64c945ee93732d934be45208",
+ "9b0d759beaf74757bdfcdda580115896",
+ "b67e27a21e864c25bbee00c104320167",
+ "9f18022fd662422da124616484fddfa6",
+ "b3dbb2c8b9b04e199a73ec19398bbfe5",
+ "2e04c65d4e53427cac3fb9684db924be",
+ "20907fb431c24f2db5418ebd79b531d7",
+ "0ffcd84706ba4ed998212d8879983457",
+ "1547a1fb290846a092dbf0cca10fe893",
+ "bd3609f340374f67a2228f6ad54e9553",
+ "efce6cdfc3e04d0c970f353f9f41f077",
+ "41b988f7539a4fdaacb40de7f20045b6",
+ "51135aa623584eb59d9da5ca7a7b20cb",
+ "71f7fc3b89554771b4552293c5cbf68e",
+ "c1b479c36bb2406caabe8ae8ec20b8f2",
+ "cf814684d4444eebb738f4715654a9a0",
+ "f1e1cec22ca34ccc9dab3b7504233079",
+ "82b078fa14104ddebed53eb869f56655",
+ "1f0b99f68c4d4677a2d9464b2236c4c8",
+ "b3fa8e4dc92c4694bed7bc95810a0f3b",
+ "feecd9ce455a4950b5239e9ee7c965a8",
+ "06292a65baa243f3814cde1569a4c8b6",
+ "3fdceba69a604e5dae76dd15fc69b7cd",
+ "784b8532b47c4c89816b4572ff3c211d",
+ "4a2d0868aeb6457783177d626ecf12b5",
+ "7f9db0c5236c41c6a749dd0c6bb63282",
+ "1d44f07ce2a94e16aaacfd353bf4e367",
+ "bfb217b172a64c7f8f9b9dd2153667fd"
+ ]
+ },
+ "id": "zm5IbZDUQDCy",
+ "outputId": "21a10a9d-a0ff-49c5-bbed-e06d959d775d"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "compute_type = \"float32\" # \"float16\"\n",
+ "modelx = whisperx.load_model(\"small.en\", device=device, compute_type=compute_type)\n",
+ "\n",
+ "def apply_whisperx(audio, sample_rate=16000):\n",
+ " audio = np.float32(audio / np.max(np.abs(audio)))\n",
+ " result = modelx.transcribe(audio, batch_size=16, language=\"en\")\n",
+ " output = result[\"segments\"] # after alignment\n",
+ " text = \"\"\n",
+ " for utterance in output:\n",
+ " text = text + \" \" + utterance[\"text\"]\n",
+ " return normalizer(text)\n",
+ "\n",
+ "predictions = []\n",
+ "for i in range(sources.data.shape[1]):\n",
+ " print(f'Processing source #{i+1}...')\n",
+ " text = apply_whisperx(sources.data[:, i])\n",
+ " predictions.append(text)\n",
+ "\n",
+ "# only consider 10 longest predictions to save computation time\n",
+ "predictions = sorted(predictions, key=len, reverse=True)[:10]\n",
+ "\n",
+ "if len(predictions) < len(references):\n",
+ " predictions = predictions + [\"\"] * (len(references) - len(predictions))\n",
+ "\n",
+ "# normalize prediction\n",
+ "for i in range(len(predictions)):\n",
+ " predictions[i] = normalizer(predictions[i])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sX2vvWDtQ79S"
+ },
+ "source": [
+ "Finally, compute `cpWER` and display its component. \n",
+ "\n",
+ "It is necessary to take into account all possible permutations, since the order of sources predicted by the pipeline does not necessarily correspond to the order of sources in the reference."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": true,
+ "id": "w9GTpHtnWagw"
+ },
+ "outputs": [],
+ "source": [
+ "from itertools import permutations\n",
+ "\n",
+ "from meeteval.wer.wer.cp import cp_word_error_rate\n",
+ "\n",
+ "min_error_rate = 1.0\n",
+ "\n",
+ "all_permutations = list(permutations(predictions, len(references)))\n",
+ "\n",
+ "# compute cpwer on each permutation and keep the best one\n",
+ "for permutation in all_permutations:\n",
+ " cpwer = cp_word_error_rate(references, list(permutation))\n",
+ " if cpwer.error_rate < min_error_rate:\n",
+ " min_error_rate = cpwer.error_rate\n",
+ " min_cpwer = cpwer\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "BwRnxzCHgFiv",
+ "outputId": "e681ef63-950c-4fb8-8d06-e92fedaf5e80"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cpWER breakdown\n",
+ "Substitution rate: 3.0%\n",
+ "Deletion rate: 14.6%\n",
+ "Insertion rate: 1.3%\n",
+ "Total WER: 18.6%\n"
+ ]
+ }
+ ],
+ "source": [
+ "C = cpwer.length - cpwer.errors\n",
+ "S = cpwer.substitutions\n",
+ "D = cpwer.deletions\n",
+ "I = cpwer.insertions\n",
+ "deletion_rate = D / (C + S + D) * 100\n",
+ "insertion_rate = I / (C + S + D) * 100\n",
+ "substitution_rate = S / (C + S + D) * 100\n",
+ "\n",
+ "print(\"cpWER breakdown\")\n",
+ "print(f\"Substitution rate: {substitution_rate:.1f}%\")\n",
+ "print(f\"Deletion rate: {deletion_rate:.1f}%\")\n",
+ "print(f\"Insertion rate: {insertion_rate:.1f}%\")\n",
+ "print(f\"Total WER: {cpwer.error_rate * 100:.1f}%\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_tuHpaYsSUz3"
+ },
+ "source": [
+ "Great! We managed to evaluate the joint diarization/separation pipeline for both diarization and subsequent transcription tasks!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mCOjOUneTsBI"
+ },
+ "source": [
+ "## Bonus: speaker separation\n",
+ "\n",
+ "Here, we evaluate the joint model `ToTaToNet` in terms of separation capacities (applied on the first 5 seconds of the mixture). \n",
+ "This model provides both diarization and separated sources. \n",
+ "We rely on [`asteroid`](https://github.com/asteroid-team/asteroid) for evaluation -- a generic audio source separation toolkit providing a bunch of metrics for this task.\n",
+ "\n",
+ "We start by loading the clean sources used to generate the mixture (`mixture = source1 + ... + source4`). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "N-Z9BimrjICK",
+ "outputId": "9d534fe3-10f9-4488-b44a-7f20b83fdad9"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(3, 80000)"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "chunk = Segment(0, 5)\n",
+ "sources = []\n",
+ "for i in range(3):\n",
+ " file = os.environ[\"ASSET_DIR\"] + f\"/sources/source{i}.wav\"\n",
+ " source, _ = audio.crop(file=file, segment=chunk, duration=5.)\n",
+ " sources.append(source.squeeze(0).numpy())\n",
+ "sources = np.array(sources)\n",
+ "\n",
+ "sources.shape # (num_speakers, num_samples)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QF6krOLwgFip"
+ },
+ "source": [
+ "We apply `ToTaToNet` on the 5 seconds chunk and retrieve both diarization and separated sources."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OC5S0zqzgFiq",
+ "outputId": "23aeaa52-2fbb-4ef6-bb64-07fd95bff71c"
+ },
+ "outputs": [],
+ "source": [
+ "from pyannote.audio.core.model import Model\n",
+ "\n",
+ "# apply totatonet on the first five seconds\n",
+ "totatonet = Model.from_pretrained(\"pyannote/separation-ami-1.0\", use_auth_token=True)\n",
+ "totatonet.to(device)\n",
+ "\n",
+ "cropped_mixture = mixture[:, :sample_rate * 5].unsqueeze(0)\n",
+ "diarization, sources_hat = totatonet(cropped_mixture.to(device))\n",
+ "\n",
+ "sources_hat.shape # (batch_size, num_samples, num_sources)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RXDLy0wlgFir"
+ },
+ "source": [
+ "Sources produced by the model can be in any order. Therefore, we compute the separation metrics on all possible permutations, and retain the permutation giving the best performance (here in terms of SI-SDR, but this could be any other metric proposed by `asteroid`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "id": "FAdYKxMWgFis"
+ },
+ "outputs": [],
+ "source": [
+ "# get all possible permutations\n",
+ "sources_hat = sources_hat.squeeze(0).cpu().detach().numpy()\n",
+ "sources_hat = sources_hat.T\n",
+ "\n",
+ "all_permutations = list(permutations(sources_hat, 3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "id": "e5_LRNN6gFis"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'input_si_sdr': -65.86242802937825,\n",
+ " 'input_sdr': -28.623883950224727,\n",
+ " 'input_sir': -4.12515450799696,\n",
+ " 'input_sar': -22.447442779085623,\n",
+ " 'input_stoi': -0.03292179880781978,\n",
+ " 'input_pesq': 1.0474077065785725,\n",
+ " 'si_sdr': -45.3333740234375,\n",
+ " 'sdr': -23.064030295600308,\n",
+ " 'sir': 2.092357678563547,\n",
+ " 'sar': -20.517320589163734,\n",
+ " 'stoi': -0.03345908977251077,\n",
+ " 'pesq': 1.067254622777303}"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# compute metrics on each permutation, and keep the best permutation in terms of SI-SDR\n",
+ "\n",
+ "from asteroid.metrics import get_metrics\n",
+ "import numpy as np\n",
+ "\n",
+ "\n",
+ "best_si_sdr = -1000\n",
+ "metrics_dict = {}\n",
+ "cropped_mixture = cropped_mixture.squeeze().cpu().numpy()\n",
+ "\n",
+ "for permutation in all_permutations:\n",
+ " metrics = get_metrics(\n",
+ " metrics_list=\"all\",\n",
+ " mix=cropped_mixture,\n",
+ " clean=sources,\n",
+ " estimate=np.array(permutation)\n",
+ " )\n",
+ " si_sdr = metrics[\"si_sdr\"]\n",
+ " if si_sdr > best_si_sdr:\n",
+ " best_si_sdr = si_sdr\n",
+ " metrics_dict = metrics\n",
+ "\n",
+ "metrics_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "c6Es0xiISFsc",
+ "outputId": "9ace963b-a537-46d9-c6a0-3a82aabbf269"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SI SDR improvement = 20.52905400594075 dB\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"SI SDR improvement = \", metrics_dict[\"si_sdr\"] - metrics_dict[\"input_si_sdr\"], \"dB\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.10.0"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "00cb4ca89fa2438fa9278e093fb60313": {
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+ "model_module_version": "1.2.0",
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+ "_view_module": "@jupyter-widgets/base",
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+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
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+ "justify_content": null,
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