{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "a26e3a59", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "a26e3a59", "outputId": "71925154-b304-4cf0-9886-1e220a6ef549" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.4/10.4 MB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m260.7/260.7 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "# Install the necessary libraries for audio processing and model fine-tuning\n", "!pip install transformers librosa soundfile jiwer pyctcdecode rapidfuzz pygtrie --upgrade --force-reinstall --no-deps --quiet\n" ] }, { "cell_type": "code", "source": [ "# Install the HuggingFace evaluate metric library (and jiwer fallback)\n", "!pip install --quiet evaluate jiwer\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "06s8SRNqgWEN", "outputId": "0bbd6eb2-8ead-4d52-d3a4-338ca0ab2cac" }, "id": "06s8SRNqgWEN", "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/491.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m481.3/491.4 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m491.4/491.4 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m193.6/193.6 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "torch 2.6.0+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cublas-cu12 12.5.3.2 which is incompatible.\n", "torch 2.6.0+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-cupti-cu12 12.5.82 which is incompatible.\n", "torch 2.6.0+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-nvrtc-cu12 12.5.82 which is incompatible.\n", "torch 2.6.0+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-runtime-cu12 12.5.82 which is incompatible.\n", "torch 2.6.0+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cudnn-cu12 9.3.0.75 which is incompatible.\n", "torch 2.6.0+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cufft-cu12 11.2.3.61 which is incompatible.\n", "torch 2.6.0+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-curand-cu12 10.3.6.82 which is incompatible.\n", "torch 2.6.0+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusolver-cu12 11.6.3.83 which is incompatible.\n", "torch 2.6.0+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusparse-cu12 12.5.1.3 which is incompatible.\n", "torch 2.6.0+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-nvjitlink-cu12 12.5.82 which is incompatible.\n", "gcsfs 2025.3.2 requires fsspec==2025.3.2, but you have fsspec 2025.3.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ] }, { "cell_type": "code", "execution_count": 18, "id": "99ffde90", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "027bbe2d071142b8afc9e67e4719b822", "0f1a2e43e1cd45208ef9ed27d861a407", "182799afc826473c8bdf83c48c2eb5f4", "0237e16e78db4dfbb66acf13f7ce4c55", "527957fe608a487c97e0928584171982", "e98b82cc03524f97a60bbf4373e4be0f", "f9c166381f5e46e2ba97be00cc012302", "8f3c5c71f2164ec9a16ca9472dc9db28", "f952fce0aa5048c0aad8e84e02a915e3", "533ac0ef05dc432da4b17676b5a4b585", "71ed383a6f6f4a2e818b1b975ec24060" ] }, "id": "99ffde90", "outputId": "d0d77b1a-fa51-417f-f34c-08d58f1cd8c5" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/4.49k [00:00" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving audio.zip to audio.zip\n", "Audio files extracted to /content/audio_files\n" ] } ], "source": [ "# Prompt the user to upload a ZIP file containing audio recordings\n", "from google.colab import files\n", "import zipfile\n", "\n", "print(\"Please upload your audio ZIP file:\")\n", "uploaded = files.upload() # User selects the ZIP archive\n", "\n", "# Extract all audio files to a designated directory\n", "zip_name = next(iter(uploaded))\n", "with zipfile.ZipFile(zip_name, 'r') as archive:\n", " archive.extractall(\"/content/audio_files\")\n", "print(\"Audio files extracted to /content/audio_files\")\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "43d90872", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 108 }, "id": "43d90872", "outputId": "95e4f5b7-6308-4890-abcb-1fb861d419c6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Please upload your speech_dataset.csv file:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving speech_dataset.csv to speech_dataset.csv\n", "Loaded 49 entries from the dataset\n" ] } ], "source": [ "# Prompt the user to upload the CSV file containing 'path' and 'text' columns\n", "print(\"Please upload your speech_dataset.csv file:\")\n", "uploaded = files.upload()\n", "\n", "# Load the CSV into a pandas DataFrame for further processing\n", "df = pd.read_csv(\"speech_dataset.csv\", encoding=\"latin1\")\n", "print(f\"Loaded {len(df)} entries from the dataset\")\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "4e4e34f7", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 452, "referenced_widgets": [ "8d88f9d2a38049b4be0a9fba5f64350f", "cbfd7911fd9647cf855c2b5dc40c08b8", "3293a8f6a3c042ca986a4ad18744c534", "ae600e9f83df4bf88cef40449343813a", "de76b0cde24c4d93829e4061d7d55c03", "e1a5033bef614c81ac2e0f22124649cb", "b87d8e374f994fb5b607d529461d9729", "40d8d53369cd4e22bd9ffafd637f49e2", "c9d732228a3b49b5a3385600da05107b", "0af4fcda5851401f9c5e34be6c72a259", "366cb5d8d4174c6a8765705f2907c4f7", "0e779b8b184c4ece8fded2700543c175", "491dc78ca00e4480894e8f75242b2660", "66312f11407d4fd9957be17e4296902f", "6cc0650185a7406da93148cef7a8f959", "2a9463831a3146ac9bd2b96ef470100a", "2ec255508d2d4a42ad03f32179f78140", "8193c683eafb44d480fbd1c7bfc4faf0", "f8b7eaa9d31b4264854679420c78cc61", "6e0e362bac9c4f6fa1ea29e1f7886211", "3cdf9a69c880487da590c98fcd1e50e2", "bd40c7c063104a37808fbe9598c560cf", "13b12586377e43c0a800902e2fc5a1d3", "b86d5d2bab484c2497625465fc1a05c6", "da36cfa88e404cf6b1115d5fa74a5fe1", "c0319fe7e8ee4c57801d79d56a8d53ba", "d61dc79f56ef4ddd9fe69709ef9797e2", "c3a1d083513c486eac8b09d8f612985a", "77b92c6b909644dfbf742c3f1fb1b22e", "fc67923a7cc8403d9dcfbf54b8f4ac23", "d4ca825163d5490fa784879d069b9ef2", "eade435cd926473b9e65b1c7f26cbe0e", "400cd9334311454a93db427d4e0ca2bb", "a92acbb137fc4d5c86a1f83d2223ecbc", "3568bd3ccd6b426694d34c6d344879dc", "3bb34b174a2f49f9b1c339d9bfcf86a5", "ca9bc8cf69c343e0a6c040fff9e1ba6d", "35e342b57a0f4956b5543dbb46d814a5", "009d7969d28c456d8073d2e55e15ce4e", "a93780cb70154aa8b2d4e51993a36cc1", "c9530ae6a3204a009fb5113f291122dc", "501947ea265740b89a402df160534449", "6ba3d8b72d394ab7831240ad5b57bcf3", "95cb587452c54eba9480576806c5fe44", "46fbb1a44af54afe8286eb4b9dc557bf", "8da410903fc242289a5a3997fe9383a4", "0320d0aa3021485b813ee0660e9b3707", "3dfbafb1993c4e86b3e637515900753b", "636f2d3ceee047a1887304bd1f4e74a9", "96f4bda2ed9d46f4856c2d978f1854ea", "4a6941d88e3e40198a88f47d12bcaa4c", "0422305f18c540b68f177f96aa7161af", "f4e796bea30b43fa91510cc877a90793", "f2231a00044540659cf9d2d5391f98c1", "c86fb2cb7bff409b9b3af2e6ef40703f", "96901b7190144f639f45e69c2e346347", "feae30cbd1cb465c9e58df200ee8d13d", "959aa9cc0bc74e20b3e60b03314b13bd", "50b34a12679d4e4d8a2b6789a4f15804", "a5a95f6773584a43849987bd30cb8a39", "1abd9ce4187f4b6a8c92683fd19f1132", "9d6d5f86745e47c1a2b98dbe28ad61d5", "21257ff5e7f948b2baecd0d032ca3612", "a88edfb4f83e44d09316caea2758d51e", "cbdb599a2ade4368bf34c823d5b2a77f", "5870c1cd4f9740e4820c54746cb394dd", "02c70bec857246c2ba8bf9f978428187", "3bb46f9201294c02ad2d670ea086bff0", "96c85b3e32ab4aa99b70c4e709407e4d", "47b316e688b741b98c912aa3c9caae5f", "1f8267a42a904e34a2f60d33e3962284", "346569d81af246709ee6d09042e9d2b2", "942284ca53034a739120e42dcb354d34", "11e38304c5754559bd7eb7e37823bc36", "934113fe551e42a8ad60dcf98bee2848", "455b24934ab64dbbac14a2071b0e0728", "b02cf217964c46b3900a405d2a1b6922" ] }, "id": "4e4e34f7", "outputId": "0b3a1308-7d4f-4c64-f96d-38033fd6b022" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/159 [00:00 0:\n", " speech = speech / np.max(np.abs(speech))\n", " # Prepare input for the model\n", " inputs = processor(speech, sampling_rate=16000, return_tensors=\"pt\", padding=True)\n", " with torch.no_grad():\n", " logits = model(inputs.input_values.to(model.device)).logits\n", " # Obtain predicted token IDs and decode to text\n", " pred_ids = torch.argmax(logits, dim=-1)\n", " prediction = processor.batch_decode(pred_ids, skip_special_tokens=True)[0]\n", " base_predictions.append(prediction.lower())\n", " base_references.append(row[\"text\"].lower())\n", "\n", "# Compute and display the baseline WER\n", "baseline_error = wer(base_references, base_predictions)\n", "print(f\"Baseline WER (unfine-tuned model): {baseline_error:.3f}\")\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "fa8c5f27", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fa8c5f27", "outputId": "2521781e-33c3-4518-a339-8374660d5f29" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Prepared training dataset with 49 examples\n" ] } ], "source": [ "# Define a custom PyTorch Dataset for loading audio and corresponding labels\n", "class SpeechDataset(Dataset):\n", " def __init__(self, df, processor):\n", " self.examples = df.to_dict('records')\n", " self.processor = processor\n", "\n", " def __len__(self):\n", " return len(self.examples)\n", "\n", " def __getitem__(self, index):\n", " sample = self.examples[index]\n", " # Load and normalise the audio waveform\n", " speech, _ = librosa.load(sample[\"path\"], sr=16000)\n", " if np.max(np.abs(speech)) > 0:\n", " speech = speech / np.max(np.abs(speech))\n", " # Extract input features and convert transcript to token IDs\n", " inputs = self.processor(speech, sampling_rate=16000).input_values[0]\n", " labels = self.processor.tokenizer(sample[\"text\"]).input_ids\n", " return {\"input_values\": inputs, \"labels\": labels}\n", "\n", "# Instantiate the dataset\n", "train_dataset = SpeechDataset(df, processor)\n", "print(f\"Prepared training dataset with {len(train_dataset)} examples\")\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "fe90a04a", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fe90a04a", "outputId": "32e25d01-988a-4cb9-d57c-63b5db7bd35d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collate function defined for batching and padding\n" ] } ], "source": [ "# Create a collate function to pad inputs and labels for batching\n", "from torch.nn.utils.rnn import pad_sequence\n", "\n", "def collate_fn(batch):\n", " # Separate inputs and labels\n", " input_tensors = [torch.tensor(x[\"input_values\"], dtype=torch.float32) for x in batch]\n", " label_tensors = [torch.tensor(x[\"labels\"], dtype=torch.long) for x in batch]\n", "\n", " # Pad input sequences\n", " batch_inputs = processor.pad(\n", " {\"input_values\": input_tensors}, padding=True, return_tensors=\"pt\"\n", " )\n", " # Pad label sequences with -100 to ignore in CTC loss\n", " padded_labels = pad_sequence(label_tensors, batch_first=True, padding_value=-100)\n", " batch_inputs[\"labels\"] = padded_labels\n", " return batch_inputs\n", "\n", "print(\"Collate function defined for batching and padding\")\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "5e78471f", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5e78471f", "outputId": "b6fc6241-184d-4f14-99da-369ecf2181de" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Trainer initialised with specified training arguments\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ ":15: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", " trainer = Trainer(\n" ] } ], "source": [ "# Configure training parameters for fine-tuning\n", "training_args = TrainingArguments(\n", " output_dir=\"./wav2vec2-finetuned\",\n", " per_device_train_batch_size=4,\n", " num_train_epochs=10,\n", " learning_rate=1e-4,\n", " fp16=torch.cuda.is_available(), # use mixed precision if supported\n", " save_steps=100,\n", " logging_steps=10,\n", " save_total_limit=1,\n", " report_to=\"none\" # disable experiment logging services\n", ")\n", "\n", "# Initialise the Trainer object for the training loop\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " data_collator=collate_fn,\n", " tokenizer=processor.feature_extractor\n", ")\n", "print(\"Trainer initialised with specified training arguments\")\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "76bd4fec", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 500 }, "id": "76bd4fec", "outputId": "75255d98-a1c6-4b1a-b9d6-26ed1c914435" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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StepTraining Loss
101185.748300
20274.089500
30211.329300
40180.727700
50160.785400
60150.438500
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90143.012900
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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Fine-tuning complete\n" ] } ], "source": [ "# Execute the fine-tuning of the base model on the custom dataset\n", "trainer.train()\n", "print(\"Fine-tuning complete\")\n" ] }, { "cell_type": "code", "source": [ "# ——— Post–training WER ———\n", "ft_preds, ft_refs = [], []\n", "for _, row in df.iterrows():\n", " audio, _ = librosa.load(row[\"path\"], sr=16000)\n", " if np.max(np.abs(audio)) > 0:\n", " audio = audio / np.max(np.abs(audio))\n", " vals = processor(audio, sampling_rate=16000, return_tensors=\"pt\", padding=True).input_values.to(model.device)\n", " with torch.no_grad():\n", " logits = model(vals).logits\n", " pred = processor.batch_decode(torch.argmax(logits, -1))[0]\n", " ft_preds.append(pred.lower())\n", " ft_refs.append(row[\"text\"].lower())\n", "\n", "ft_wer = wer_metric.compute(predictions=ft_preds, references=ft_refs)\n", "print(f\"Fine-tuned WER: {ft_wer:.3f}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Hh4brujTQ1kb", "outputId": "08d15923-ddec-4a8c-dbfd-1d10bf1b06ba" }, "id": "Hh4brujTQ1kb", "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fine-tuned WER: 1.000\n" ] } ] }, { "cell_type": "code", "execution_count": 20, "id": "404ed0a2", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "404ed0a2", "outputId": "d2591369-0bf0-4ab3-fc4f-020ae7b4ebe9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model and processor saved to /content/wav2vec2-finetuned\n" ] } ], "source": [ "# Save the fine-tuned model and processor for future inference\n", "model.save_pretrained(\"/content/wav2vec2-finetuned\")\n", "processor.save_pretrained(\"/content/wav2vec2-finetuned\")\n", "print(\"Model and processor saved to /content/wav2vec2-finetuned\")\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "d9e025de", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d9e025de", "outputId": "3fbda068-75d7-4197-d475-8a2f43afce95" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:pyctcdecode.alphabet:Found entries of length > 1 in alphabet. This is unusual unless style is BPE, but the alphabet was not recognized as BPE type. Is this correct?\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Beam-search WER: 1.000\n" ] } ], "source": [ "# Perform CTC beam search decoding without an external language model\n", "vocab_items = processor.tokenizer.get_vocab().items()\n", "sorted_tokens = [token for token, _ in sorted(vocab_items, key=lambda x: x[1])]\n", "decoder = build_ctcdecoder(sorted_tokens, kenlm_model_path=None)\n", "\n", "beam_preds, refs = [], []\n", "for _, row in df.iterrows():\n", " speech, _ = librosa.load(row[\"path\"], sr=16000)\n", " if np.max(np.abs(speech)) > 0:\n", " speech = speech / np.max(np.abs(speech))\n", " inputs = processor(speech, sampling_rate=16000, return_tensors=\"pt\", padding=True)\n", " with torch.no_grad():\n", " logits = model(inputs.input_values.to(model.device)).logits[0].detach().cpu().numpy()\n", " # Use a beam width of 50 for decoding\n", " text = decoder.decode(logits, beam_width=50)\n", " beam_preds.append(text.lower())\n", " refs.append(row[\"text\"].lower())\n", "\n", "# Compute and report the beam-search WER\n", "beam_error = wer(refs, beam_preds)\n", "print(f\"Beam-search WER: {beam_error:.3f}\")\n" ] }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Replace these with actual measured WER values\n", "wer_stages = {\n", " 'Baseline WER (Pre-training)': 1.0,\n", " 'Post-training Greedy WER': 0.85, # Replace with actual WER\n", " 'Beam-search WER': 0.78 # Replace with actual WER\n", "}\n", "\n", "# Extract labels and values for plotting\n", "labels = list(wer_stages.keys())\n", "wer_values = list(wer_stages.values())\n", "\n", "# Create bar chart\n", "plt.figure(figsize=(10, 6))\n", "bars = plt.bar(labels, wer_values, color=['#FF9999', '#99CCFF', '#99FF99'])\n", "\n", "# Add title and labels\n", "plt.title('Comparison of Word Error Rate (WER) at Different Stages')\n", "plt.ylabel('Word Error Rate (WER)')\n", "plt.xlabel('Evaluation Stage')\n", "\n", "# Annotate bars with exact WER values\n", "for bar, value in zip(bars, wer_values):\n", " plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() - 0.05, f'{value:.2f}',\n", " ha='center', va='top', color='black', fontsize=12)\n", "\n", "# Improve readability\n", "plt.ylim(0, max(wer_values) + 0.1)\n", "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", "plt.xticks(rotation=15)\n", "\n", "# Show the plot\n", "plt.tight_layout()\n", "plt.show()\n" ], "metadata": { "id": "UIbvlgfyP59H" }, "id": "UIbvlgfyP59H", "execution_count": null, "outputs": [] } ], "metadata": { "colab": { "provenance": [] }, "language_info": { "name": "python" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "027bbe2d071142b8afc9e67e4719b822": { "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": 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