evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-sRoTHHrVlh5"
   },
   "source": [
    "Based on \"Multi-Dataset Evaluation with 🤗 Transformers and Datasets\" by [Sanchit Gandhi](https://huggingface.co/sanchit-gandhi)"
   ]
  },
  {
   "cell_type": "code",
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    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yF4v_-jQpziy",
    "outputId": "3ed33a1e-d8ab-4551-b89e-d317dc0b11f5"
   },
   "outputs": [],
   "source": [
    "!pip install datasets transformers evaluate huggingface_hub jiwer soundfile librosa\n",
    "\n",
    "# If true, will use dynamic audio_ctx based on input length\n",
    "# If false, will run the model normally (padding to 30sec)\n",
    "USE_AUDIO_CTX = True\n",
    "\n",
    "# Type of model, must match LOAD_FROM if set\n",
    "MODEL_TYPE = \"openai/whisper-small.en\"\n",
    "\n",
    "# Load from path (for finetuned model), or set to None if you'd like to use the normal one\n",
    "LOAD_FROM = \"/workspace/acft-small.en\"\n",
    "#LOAD_FROM = None\n",
    "\n",
    "# Original results are with ADD_AUDIO_CTX = 8\n",
    "ADD_AUDIO_CTX = 8"
   ]
  },
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    "id": "pkyYctmItQTF",
    "outputId": "66ddc327-39a1-4e5e-9434-9da66cbe6a70"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "librispeech_clean = load_dataset(\"librispeech_asr\", \"all\", split=\"test.clean\", streaming=True)\n",
    "librispeech_other = load_dataset(\"librispeech_asr\", \"all\", split=\"test.other\", streaming=True)\n",
    "\n",
    "voxpopuli = load_dataset(\"facebook/voxpopuli\", \"en\", split=\"test\", streaming=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BmcVXn4amR69"
   },
   "outputs": [],
   "source": [
    "esb_datasets = {\n",
    "    \"LibriSpeech Clean\": librispeech_clean,\n",
    "    \"LibriSpeech Other\": librispeech_other,\n",
    "    \"VoxPopuli\": voxpopuli\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FjBVrFve1iXF"
   },
   "outputs": [],
   "source": [
    "def get_text(sample):\n",
    "    if \"text\" in sample:\n",
    "        return sample[\"text\"]\n",
    "    elif \"sentence\" in sample:\n",
    "        return sample[\"sentence\"]\n",
    "    elif \"normalized_text\" in sample:\n",
    "        return sample[\"normalized_text\"]\n",
    "    elif \"transcript\" in sample:\n",
    "        return sample[\"transcript\"]\n",
    "    else:\n",
    "        raise ValueError(f\"Sample: {sample.keys()} has no transcript.\")"
   ]
  },
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    "id": "1wk3yAmsYwoF",
    "outputId": "99360346-2df8-45d7-8b64-36d7fac132c1"
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   "source": [
    "from transformers import pipeline, WhisperForConditionalGeneration\n",
    "\n",
    "whisper_asr = pipeline(\"automatic-speech-recognition\", model=MODEL_TYPE, device=0)\n",
    "\n",
    "device = whisper_asr.device\n",
    "\n",
    "if LOAD_FROM:\n",
    "    whisper_asr.model = WhisperForConditionalGeneration.from_pretrained(LOAD_FROM).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Qari1Lu0Dxl"
   },
   "source": [
    "## Load the Word Error Rate metric\n",
    "\n",
    "We'll assess our system using the [Word Error Rate (WER)](https://huggingface.co/spaces/evaluate-metric/wer) metric, the 'de-facto' metric for assessing ASR systems. We'll load the WER metric from the 🤗 Evaluate library:"
   ]
  },
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    "id": "XXEe44U1z7Te",
    "outputId": "bfca731a-caf4-4474-c55d-addc78d355a2"
   },
   "outputs": [],
   "source": [
    "import evaluate\n",
    "\n",
    "wer_metric = evaluate.load(\"wer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ia6-sRSClqqh"
   },
   "source": [
    "Bonus: You can also try other evaluation methods like the [Character Error Rate (CER)](https://huggingface.co/spaces/evaluate-metric/cer). For the CER, update the above statement to `evaluate.load(\"cer\")`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "63i2XhSSr3xk"
   },
   "source": [
    "## Normalisation\n",
    "\n",
    "The [Whisper paper](https://cdn.openai.com/papers/whisper.pdf) demonstrates the drastic effect that normalising the text outputs have on WER. The normalisation step is important as it removes errors unrelated to the speech recognition task, such as casing and punctuation. It also makes the formatting consistent between references and predictions by converting spelled out numbers to symbollic form (e.g. \"two\" -> \"2\") and British spellings to American (e.g. \"grey\" -> \"gray\").\n",
    "\n",
    "We first write a function to normalise the reference of a single sample according to the Whisper English text normaliser:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TZ83oC4ZihEq"
   },
   "outputs": [],
   "source": [
    "whisper_norm = whisper_asr.tokenizer._normalize\n",
    "\n",
    "def normalise(batch):\n",
    "    batch[\"norm_text\"] = whisper_norm(get_text(batch))\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cx4jbs1Epv4k"
   },
   "source": [
    "We'll apply this function to our data using 🤗 Datasets' [`.map`](https://huggingface.co/docs/datasets/process#map) method in our evaluation pipeline.\n",
    "\n",
    "We also need to remove any empty reference transcriptions from our dataset, as these will give a divide by 0 error in the WER calculation.\n",
    "\n",
    "We write a function that indicates which samples to keep, and which to discard. This function, `is_target_text_in_range`, returns a boolean: reference transcriptions that are not empty return True, and those are empty return False:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p_IohuslpiTi"
   },
   "outputs": [],
   "source": [
    "filter_sequences = [\"ignore time segment in scoring\", \"\"]\n",
    "\n",
    "def is_target_text_in_range(ref):\n",
    "    ref = ref.strip()\n",
    "    return ref not in filter_sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HJhrDucxQfYX"
   },
   "source": [
    "Again, we'll apply this function to our data using 🤗 Datasets' [`.map`](https://huggingface.co/docs/datasets/process#map) method in our evaluation pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R7Ncs3EsPe7Z"
   },
   "source": [
    "## Multi-Dataset Evaluation\n",
    "\n",
    "In this final section, we combine everything together to form the multi-dataset evaluation loop for the Whisper model.\n",
    "\n",
    "First, we define a generator that iterates over the dataset and yields the audio samples and reference text ready for our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_rQ8s45sNYfu"
   },
   "outputs": [],
   "source": [
    "def data(dataset):\n",
    "    for i, item in enumerate(dataset):\n",
    "        yield {**item[\"audio\"], \"reference\": item[\"norm_text\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import Audio\n",
    "from torch import nn\n",
    "import torch\n",
    "from transformers.modeling_outputs import BaseModelOutput\n",
    "\n",
    "def compute_partially_encoder(model, data, n_audio_ctx):\n",
    "  diffy = 2*n_audio_ctx - data.shape[2]\n",
    "\n",
    "  if diffy > 0:\n",
    "    data = nn.functional.pad(data, [0, diffy, 0, 0, 0, 0], \"constant\", 0.0)\n",
    "  elif diffy < 0:\n",
    "    data = data[:,:,:diffy]\n",
    "\n",
    "  if n_audio_ctx == 1500:\n",
    "    return model.encoder(data)\n",
    "\n",
    "  input_embeds = nn.functional.gelu(model.encoder.conv1(data))\n",
    "  input_embeds = nn.functional.gelu(model.encoder.conv2(input_embeds))\n",
    "  input_embeds = input_embeds.permute(0, 2, 1)\n",
    "\n",
    "  embed_pos = model.encoder.embed_positions.weight[:n_audio_ctx]\n",
    "\n",
    "  hidden_states = input_embeds + embed_pos\n",
    "  hidden_states = nn.functional.dropout(hidden_states, p=model.encoder.dropout, training=model.encoder.training)\n",
    "\n",
    "  for idx, encoder_layer in enumerate(model.encoder.layers):\n",
    "    to_drop = False\n",
    "    if model.encoder.training:\n",
    "      dropout_probability = torch.rand([])\n",
    "      if dropout_probability < model.encoder.layerdrop:\n",
    "        to_drop = True\n",
    "\n",
    "    if to_drop:\n",
    "        layer_outputs = (None, None)\n",
    "    else:\n",
    "        if model.encoder.gradient_checkpointing and model.encoder.training:\n",
    "            layer_outputs = model.encoder._gradient_checkpointing_func(\n",
    "                encoder_layer.__call__,\n",
    "                hidden_states,\n",
    "                None,\n",
    "                None,\n",
    "                False,\n",
    "            )\n",
    "        else:\n",
    "            layer_outputs = encoder_layer(\n",
    "                hidden_states,\n",
    "                None,\n",
    "                layer_head_mask=None,\n",
    "                output_attentions=False,\n",
    "            )\n",
    "\n",
    "        hidden_states = layer_outputs[0]\n",
    "\n",
    "  hidden_states = model.encoder.layer_norm(hidden_states)\n",
    "  return BaseModelOutput(last_hidden_state=hidden_states)\n",
    "\n",
    "\n",
    "def whisper_asr_partial(whisper_asr, data, batch_size=0):\n",
    "    for data in data:\n",
    "        waveform = data[\"array\"]\n",
    "        sampling_rate = data[\"sampling_rate\"]\n",
    "        \n",
    "        # Use the model and processor to transcribe the audio:\n",
    "        input_features = whisper_asr.feature_extractor(\n",
    "            waveform, sampling_rate=sampling_rate, return_tensors=\"pt\"\n",
    "        ).input_features.to(whisper_asr.model.model.device)\n",
    "\n",
    "        length = len(waveform) / sampling_rate\n",
    "\n",
    "        n_ctx = min(1500, int(round((1500.0 / 30.0) * length )) + ADD_AUDIO_CTX)\n",
    "        encoder_hidden_states_partial = compute_partially_encoder(whisper_asr.model.model, input_features, n_ctx)\n",
    "        tokens = whisper_asr.model.generate(encoder_outputs=encoder_hidden_states_partial)\n",
    "        yield {\"text\": whisper_asr.tokenizer.batch_decode(tokens, skip_special_tokens=True)[0], \"reference\": [data[\"reference\"]]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8qLWCi_MoVrv",
    "outputId": "acb3cd49-09a6-40df-df95-bbc9251c80ab"
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "# set the batch size in accordance to your device\n",
    "BATCH_SIZE = 1\n",
    "wer_results = []\n",
    "\n",
    "t0 = time.time()\n",
    "# loop over all the datasets in the ESB benchmark\n",
    "for dataset_name, dataset in esb_datasets.items():\n",
    "    # only for debugging, restricts the number of rows to numeric value in brackets\n",
    "    dataset = dataset.take(128)\n",
    "\n",
    "    # resample to 16kHz\n",
    "    dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
    "\n",
    "    # normalise references\n",
    "    dataset = dataset.map(normalise)\n",
    "\n",
    "    # remove any empty references\n",
    "    dataset = dataset.filter(is_target_text_in_range, input_columns=[\"norm_text\"])\n",
    "\n",
    "    # placeholders for predictions and references\n",
    "    predictions = []\n",
    "    references = []\n",
    "\n",
    "    # run streamed inference\n",
    "    if USE_AUDIO_CTX:\n",
    "        for out in whisper_asr_partial(whisper_asr, data(dataset), batch_size=BATCH_SIZE):\n",
    "            predictions.append(whisper_norm(out[\"text\"]))\n",
    "            references.append(out[\"reference\"][0])\n",
    "    else:\n",
    "        for out in whisper_asr(data(dataset), batch_size=BATCH_SIZE):\n",
    "            predictions.append(whisper_norm(out[\"text\"]))\n",
    "            references.append(out[\"reference\"][0])\n",
    "\n",
    "    # compute the WER\n",
    "    wer = wer_metric.compute(references=references, predictions=predictions)\n",
    "    wer = round(100 * wer, 2)\n",
    "\n",
    "    wer_results.append(wer)\n",
    "t1 = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "2pw9sz3Xujdj",
    "outputId": "eae533d1-f0d9-42b6-94a4-4fdbefb06fac"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "print(\"Time:\",t1-t0)\n",
    "df = pd.DataFrame({\"Dataset\": esb_datasets.keys(), \"WER\": wer_results})\n",
    "df"
   ]
  }
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