notebooks/227-whisper-subtitles-generation/227-whisper-subtitles-generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Video Subtitle Generation using Whisper and OpenVINO™\n",
    "\n",
    "[Whisper](https://openai.com/blog/whisper/)  is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web.  It is a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.\n",
    "\n",
    "![asr-training-data-desktop.svg](https://user-images.githubusercontent.com/29454499/204536347-28976978-9a07-416c-acff-fc1214bbfbe0.svg)\n",
    "\n",
    "You can find more information about this model in the [research paper](https://cdn.openai.com/papers/whisper.pdf), [OpenAI blog](https://openai.com/blog/whisper/), [model card](https://github.com/openai/whisper/blob/main/model-card.md) and GitHub [repository](https://github.com/openai/whisper).\n",
    "\n",
    "In this notebook we will use Whisper with OpenVINO to generate subtitles in a sample video.\n",
    "Notebook contains following steps:\n",
    "1. Download model\n",
    "2. Instantiate PyTorch model pipeline\n",
    "3. Export ONNX model and convert to OpenVINO IR using the Model Optimizer tool\n",
    "4. Run Whisper pipeline with OpenVINO models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clone and install model repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install \"python-ffmpeg<=1.0.16\" moviepy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "REPO_DIR = Path(\"whisper\")\n",
    "if not REPO_DIR.exists():\n",
    "    !git clone https://github.com/openai/whisper.git -b v20230124\n",
    "%cd whisper\n",
    "!python setup.py develop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate model\n",
    "Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It maps a sequence of audio spectrogram features to a sequence of text tokens. First, the raw audio inputs are converted to a log-Mel spectrogram by action of the feature extractor. The Transformer encoder then encodes the spectrogram to form a sequence of encoder hidden states. Finally, the decoder autoregressively predicts text tokens, conditional on both the previous tokens and the encoder hidden states.\n",
    "\n",
    "You can see the model architecture in the diagram below:\n",
    "\n",
    "![whisper_architecture.svg](https://user-images.githubusercontent.com/29454499/204536571-8f6d8d77-5fbd-4c6d-8e29-14e734837860.svg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several models of different sizes and capabilities trained by the model authors. In this tutorial we will use the `base` model, but the same actions are also applicable to other models from Whisper family."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import whisper\n",
    "\n",
    "model = whisper.load_model(\"base\")\n",
    "model.to(\"cpu\")\n",
    "model.eval()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert model to OpenVINO Intermediate Representation (IR) format.\n",
    "\n",
    "For best results with OpenVINO we recommend converting the model to OpenVINO IR format. OpenVINO supports PyTorch via ONNX conversion. We will use `torch.onnx.export` for exporting the ONNX model from PyTorch. We need to provide initialized model object and example of inputs for shape inference. We will use `mo.convert_model` functionality to convert the ONNX models. The `mo.convert_model` Python function returns an OpenVINO model ready to load on device and start making predictions. We can save it on disk for next usage with `openvino.runtime.serialize`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Whisper Encoder to OpenVINO IR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from openvino.tools import mo\n",
    "from openvino.runtime import serialize\n",
    "\n",
    "mel = torch.zeros((1, 80, 3000))\n",
    "audio_features = model.encoder(mel)\n",
    "torch.onnx.export(\n",
    "    model.encoder, \n",
    "    mel, \n",
    "    \"whisper_encoder.onnx\",\n",
    "    input_names=[\"mel\"], \n",
    "    output_names=[\"output_features\"]\n",
    ")\n",
    "encoder_model = mo.convert_model(\"whisper_encoder.onnx\", compress_to_fp16=True)\n",
    "serialize(encoder_model, xml_path=\"whisper_encoder.xml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Whisper decoder to OpenVINO IR\n",
    "\n",
    "To reduce computational complexity, the decoder uses cached key/value projections in attention modules from the previous steps. We need to modify this process for correct tracing to ONNX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from typing import Optional, Union, List, Dict\n",
    "from functools import partial\n",
    "\n",
    "positional_embeddings_size = model.decoder.positional_embedding.shape[0]\n",
    "\n",
    "\n",
    "def save_to_cache(cache: Dict[str, torch.Tensor], module: str, output: torch.Tensor):\n",
    "    \"\"\"\n",
    "    Saving cached attention hidden states for previous tokens.\n",
    "    Parameters:\n",
    "      cache: dictionary with cache.\n",
    "      module: current attention module name.\n",
    "      output: predicted hidden state.\n",
    "    Returns:\n",
    "      output: cached attention hidden state for specified attention module.\n",
    "    \"\"\"\n",
    "    if module not in cache or output.shape[1] > positional_embeddings_size:\n",
    "        # save as-is, for the first token or cross attention\n",
    "        cache[module] = output\n",
    "    else:\n",
    "        cache[module] = torch.cat([cache[module], output], dim=1).detach()\n",
    "    return cache[module]\n",
    "\n",
    "\n",
    "def attention_forward(\n",
    "        attention_module,\n",
    "        x: torch.Tensor,\n",
    "        xa: Optional[torch.Tensor] = None,\n",
    "        mask: Optional[torch.Tensor] = None,\n",
    "        kv_cache: Optional[dict] = None,\n",
    "        idx: int = 0\n",
    "):\n",
    "    \"\"\"\n",
    "    Override for forward method of decoder attention module with storing cache values explicitly.\n",
    "    Parameters:\n",
    "      attention_module: current attention module\n",
    "      x: input token ids.\n",
    "      xa: input audio features (Optional).\n",
    "      mask: mask for applying attention (Optional).\n",
    "      kv_cache: dictionary with cached key values for attention modules.\n",
    "      idx: idx for search in kv_cache.\n",
    "    Returns:\n",
    "      attention module output tensor\n",
    "      updated kv_cache\n",
    "    \"\"\"\n",
    "    q = attention_module.query(x)\n",
    "\n",
    "    if kv_cache is None or xa is None:\n",
    "        # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;\n",
    "        # otherwise, perform key/value projections for self- or cross-attention as usual.\n",
    "        k = attention_module.key(x if xa is None else xa)\n",
    "        v = attention_module.value(x if xa is None else xa)\n",
    "        if kv_cache is not None:\n",
    "            k = save_to_cache(kv_cache, f'k_{idx}', k)\n",
    "            v = save_to_cache(kv_cache, f'v_{idx}', v)\n",
    "    else:\n",
    "        # for cross-attention, calculate keys and values once and reuse in subsequent calls.\n",
    "        k = kv_cache.get(f'k_{idx}', save_to_cache(\n",
    "            kv_cache, f'k_{idx}', attention_module.key(xa)))\n",
    "        v = kv_cache.get(f'v_{idx}', save_to_cache(\n",
    "            kv_cache, f'v_{idx}', attention_module.value(xa)))\n",
    "\n",
    "    wv, qk = attention_module.qkv_attention(q, k, v, mask)\n",
    "    return attention_module.out(wv), kv_cache\n",
    "\n",
    "\n",
    "def block_forward(\n",
    "    residual_block,\n",
    "    x: torch.Tensor,\n",
    "    xa: Optional[torch.Tensor] = None,\n",
    "    mask: Optional[torch.Tensor] = None,\n",
    "    kv_cache: Optional[dict] = None,\n",
    "    idx: int = 0\n",
    "):\n",
    "    \"\"\"\n",
    "    Override for residual block forward method for providing kv_cache to attention module.\n",
    "      Parameters:\n",
    "        residual_block: current residual block.\n",
    "        x: input token_ids.\n",
    "        xa: input audio features (Optional).\n",
    "        mask: attention mask (Optional).\n",
    "        kv_cache: cache for storing attention key values.\n",
    "        idx: index of current residual block for search in kv_cache.\n",
    "      Returns:\n",
    "        x: residual block output\n",
    "        kv_cache: updated kv_cache\n",
    "\n",
    "    \"\"\"\n",
    "    x0, kv_cache = residual_block.attn(residual_block.attn_ln(\n",
    "        x), mask=mask, kv_cache=kv_cache, idx=f'{idx}a')\n",
    "    x = x + x0\n",
    "    if residual_block.cross_attn:\n",
    "        x1, kv_cache = residual_block.cross_attn(\n",
    "            residual_block.cross_attn_ln(x), xa, kv_cache=kv_cache, idx=f'{idx}c')\n",
    "        x = x + x1\n",
    "    x = x + residual_block.mlp(residual_block.mlp_ln(x))\n",
    "    return x, kv_cache\n",
    "\n",
    "\n",
    "# update forward functions\n",
    "for idx, block in enumerate(model.decoder.blocks):\n",
    "    block.forward = partial(block_forward, block, idx=idx)\n",
    "    block.attn.forward = partial(attention_forward, block.attn)\n",
    "    if block.cross_attn:\n",
    "        block.cross_attn.forward = partial(attention_forward, block.cross_attn)\n",
    "\n",
    "\n",
    "def decoder_forward(decoder, x: torch.Tensor, xa: torch.Tensor, kv_cache: Optional[dict] = None):\n",
    "    \"\"\"\n",
    "    Override for decoder forward method.\n",
    "    Parameters:\n",
    "      x: torch.LongTensor, shape = (batch_size, <= n_ctx) the text tokens\n",
    "      xa: torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)\n",
    "           the encoded audio features to be attended on\n",
    "      kv_cache: Dict[str, torch.Tensor], attention modules hidden states cache from previous steps \n",
    "    \"\"\"\n",
    "    offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0\n",
    "    x = decoder.token_embedding(\n",
    "        x) + decoder.positional_embedding[offset: offset + x.shape[-1]]\n",
    "    x = x.to(xa.dtype)\n",
    "\n",
    "    for block in decoder.blocks:\n",
    "        x, kv_cache = block(x, xa, mask=decoder.mask, kv_cache=kv_cache)\n",
    "\n",
    "    x = decoder.ln(x)\n",
    "    logits = (\n",
    "        x @ torch.transpose(decoder.token_embedding.weight.to(x.dtype), 1, 0)).float()\n",
    "\n",
    "    return logits, kv_cache\n",
    "\n",
    "\n",
    "# override decoder forward\n",
    "model.decoder.forward = partial(decoder_forward, model.decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = torch.ones((5, 3), dtype=torch.int64)\n",
    "\n",
    "logits, kv_cache = model.decoder(tokens, audio_features, kv_cache={})\n",
    "kv_cache = {k: v for k, v in kv_cache.items()}\n",
    "tokens = torch.ones((5, 1), dtype=torch.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = [f\"out_{k}\" for k in kv_cache.keys()]\n",
    "inputs = [f\"in_{k}\" for k in kv_cache.keys()]\n",
    "dynamic_axes = {\n",
    "    \"tokens\": {0: \"beam_size\", 1: \"seq_len\"},\n",
    "    \"audio_features\": {0: \"beam_size\"},\n",
    "    \"logits\": {0: \"beam_size\", 1: \"seq_len\"}}\n",
    "dynamic_outs = {o: {0: \"beam_size\", 1: \"prev_seq_len\"} for o in outputs}\n",
    "dynamic_inp = {i: {0: \"beam_size\", 1: \"prev_seq_len\"} for i in inputs}\n",
    "dynamic_axes.update(dynamic_outs)\n",
    "dynamic_axes.update(dynamic_inp)\n",
    "torch.onnx.export(\n",
    "    model.decoder, {'x': tokens, 'xa': audio_features, 'kv_cache': kv_cache},\n",
    "    'whisper_decoder.onnx',\n",
    "    input_names=[\"tokens\", \"audio_features\"] + inputs,\n",
    "    output_names=[\"logits\"] + outputs,\n",
    "    dynamic_axes=dynamic_axes\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decoder model autoregressively predicts the next token guided by encoder headen states and previously predicted sequence. This means that the shape of inputs which depends on the previous step (inputs for tokens and attention hidden states from previous step) are dynamic. For efficient utilization of memory, we define an upper bound for dynamic input shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shapes = \"tokens[1..5 1..224],audio_features[1..5 1500 512]\"\n",
    "for k, v in kv_cache.items():\n",
    "    if k.endswith('a'):\n",
    "        input_shapes += f\",in_{k}[1..5 0..224 512]\"\n",
    "decoder_model = mo.convert_model(\n",
    "    input_model=\"whisper_decoder.onnx\",\n",
    "    compress_to_fp16=True,\n",
    "    input=input_shapes)\n",
    "serialize(decoder_model, \"whisper_decoder.xml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare inference pipeline\n",
    "\n",
    "The image below illustrates the pipeline of video transcribing using the Whisper model.\n",
    "\n",
    "![whisper_pipeline.png](https://user-images.githubusercontent.com/29454499/204536733-1f4342f7-2328-476a-a431-cb596df69854.png)\n",
    "\n",
    "To run the PyTorch Whisper model, you just need to call the `model.transcribe(autio, **parameters)` function. We will try to reuse original model pipeline for audio transcribing after replacing the original models with OpenVINO IR versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OpenVINOAudioEncoder(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    Helper for inference Whisper encoder model with OpenVINO\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, core, model_path, device='CPU'):\n",
    "        super().__init__()\n",
    "        self.model = core.read_model(model_path)\n",
    "        self.compiled_model = core.compile_model(self.model, device)\n",
    "        self.output_blob = self.compiled_model.output(0)\n",
    "\n",
    "    def forward(self, mel: torch.Tensor):\n",
    "        \"\"\"\n",
    "        Inference OpenVINO whisper encoder model.\n",
    "\n",
    "        Parameters:\n",
    "          mel: input audio fragment mel spectrogram.\n",
    "        Returns:\n",
    "          audio_features: torch tensor with encoded audio features.\n",
    "        \"\"\"\n",
    "        return torch.from_numpy(self.compiled_model(mel)[self.output_blob])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openvino.runtime import Core\n",
    "\n",
    "\n",
    "class OpenVINOTextDecoder(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    Helper for inference OpenVINO decoder model\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, core: Core, model_path: Path, device: str = 'CPU'):\n",
    "        super().__init__()\n",
    "        self._core = core\n",
    "        self.model = core.read_model(model_path)\n",
    "        self._input_names = [inp.any_name for inp in self.model.inputs]\n",
    "        self.compiled_model = core.compile_model(self.model, device)\n",
    "        self.device = device\n",
    "\n",
    "    def init_past_inputs(self, feed_dict):\n",
    "        \"\"\"\n",
    "        Initialize cache input for first step.\n",
    "\n",
    "        Parameters:\n",
    "          feed_dict: Dictonary with inputs for inference\n",
    "        Returns:\n",
    "          feed_dict: updated feed_dict\n",
    "        \"\"\"\n",
    "        beam_size = feed_dict['tokens'].shape[0]\n",
    "        audio_len = feed_dict['audio_features'].shape[-1]\n",
    "        previous_seq_len = 0\n",
    "        for name in self._input_names:\n",
    "            if name in ['tokens', 'audio_features']:\n",
    "                continue\n",
    "            feed_dict[name] = np.zeros(\n",
    "                (beam_size, previous_seq_len, audio_len), dtype=np.float32)\n",
    "        return feed_dict\n",
    "\n",
    "    def preprocess_kv_cache_inputs(self, feed_dict, kv_cache):\n",
    "        \"\"\"\n",
    "        Transform kv_cache to inputs\n",
    "\n",
    "        Parameters:\n",
    "          feed_dict: dictionary with inputs for inference\n",
    "          kv_cache: dictionary with cached attention hidden states from previous step\n",
    "        Returns:\n",
    "          feed_dict: updated feed dictionary with additional inputs\n",
    "        \"\"\"\n",
    "        if not kv_cache:\n",
    "            return self.init_past_inputs(feed_dict)\n",
    "        for k, v in kv_cache.items():\n",
    "            new_k = f'in_{k}'\n",
    "            if new_k in self._input_names:\n",
    "                feed_dict[new_k] = v\n",
    "        return feed_dict\n",
    "\n",
    "    def postprocess_outputs(self, outputs):\n",
    "        \"\"\"\n",
    "        Transform model output to format expected by the pipeline\n",
    "\n",
    "        Parameters:\n",
    "          outputs: outputs: raw inference results.\n",
    "        Returns:\n",
    "          logits: decoder predicted token logits\n",
    "          kv_cache: cached attention hidden states\n",
    "        \"\"\"\n",
    "        logits = None\n",
    "        kv_cache = {}\n",
    "        for output_t, out in outputs.items():\n",
    "            if 'logits' in output_t.get_names():\n",
    "                logits = torch.from_numpy(out)\n",
    "            else:\n",
    "                tensor_name = output_t.any_name\n",
    "                kv_cache[tensor_name.replace(\n",
    "                    'out_', '')] = torch.from_numpy(out)\n",
    "        return logits, kv_cache\n",
    "\n",
    "    def forward(self, x: torch.Tensor, xa: torch.Tensor, kv_cache: Optional[dict] = None):\n",
    "        \"\"\"\n",
    "        Inference decoder model.\n",
    "\n",
    "        Parameters:\n",
    "          x: torch.LongTensor, shape = (batch_size, <= n_ctx) the text tokens\n",
    "          xa: torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)\n",
    "             the encoded audio features to be attended on\n",
    "          kv_cache: Dict[str, torch.Tensor], attention modules hidden states cache from previous steps\n",
    "        Returns:\n",
    "          logits: decoder predicted logits\n",
    "          kv_cache: updated kv_cache with current step hidden states\n",
    "        \"\"\"\n",
    "        feed_dict = {'tokens': x, 'audio_features': xa}\n",
    "        feed_dict = (self.preprocess_kv_cache_inputs(feed_dict, kv_cache))\n",
    "        res = self.compiled_model(feed_dict)\n",
    "        return self.postprocess_outputs(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from whisper.decoding import DecodingTask, Inference, DecodingOptions, DecodingResult\n",
    "\n",
    "\n",
    "class OpenVINOInference(Inference):\n",
    "    \"\"\"\n",
    "    Wrapper for inference interface\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model: \"Whisper\", initial_token_length: int):\n",
    "        self.model: \"Whisper\" = model\n",
    "        self.initial_token_length = initial_token_length\n",
    "        self.kv_cache = {}\n",
    "\n",
    "    def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor) -> torch.Tensor:\n",
    "        \"\"\"\n",
    "        getting logits for given tokens sequence and audio features and save kv_cache\n",
    "\n",
    "        Parameters:\n",
    "          tokens: input tokens\n",
    "          audio_features: input audio features\n",
    "        Returns:\n",
    "          logits: predicted by decoder logits\n",
    "        \"\"\"\n",
    "        if tokens.shape[-1] > self.initial_token_length:\n",
    "            # only need to use the last token except in the first forward pass\n",
    "            tokens = tokens[:, -1:]\n",
    "        logits, self.kv_cache = self.model.decoder(\n",
    "            tokens, audio_features, kv_cache=self.kv_cache)\n",
    "        return logits\n",
    "\n",
    "    def cleanup_caching(self):\n",
    "        \"\"\"\n",
    "        Reset kv_cache to initial state\n",
    "        \"\"\"\n",
    "        self.kv_cache = {}\n",
    "\n",
    "    def rearrange_kv_cache(self, source_indices):\n",
    "        \"\"\"\n",
    "        Update hidden states cache for selected sequences\n",
    "        Parameters:\n",
    "          source_indicies: sequences indicies\n",
    "        Returns:\n",
    "          None\n",
    "        \"\"\"\n",
    "        for module, tensor in self.kv_cache.items():\n",
    "            # update the key/value cache to contain the selected sequences\n",
    "            self.kv_cache[module] = tensor[source_indices]\n",
    "\n",
    "\n",
    "class OpenVINODecodingTask(DecodingTask):\n",
    "    \"\"\"\n",
    "    Class for decoding using OpenVINO\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model: \"Whisper\", options: DecodingOptions):\n",
    "        super().__init__(model, options)\n",
    "        self.inference = OpenVINOInference(model, len(self.initial_tokens))\n",
    "\n",
    "\n",
    "@torch.no_grad()\n",
    "def decode(model: \"Whisper\", mel: torch.Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]:\n",
    "    \"\"\"\n",
    "    Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model: Whisper\n",
    "        the Whisper model instance\n",
    "\n",
    "    mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)\n",
    "        A tensor containing the Mel spectrogram(s)\n",
    "\n",
    "    options: DecodingOptions\n",
    "        A dataclass that contains all necessary options for decoding 30-second segments\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result: Union[DecodingResult, List[DecodingResult]]\n",
    "        The result(s) of decoding contained in `DecodingResult` dataclass instance(s)\n",
    "    \"\"\"\n",
    "    single = mel.ndim == 2\n",
    "    if single:\n",
    "        mel = mel.unsqueeze(0)\n",
    "\n",
    "    result = OpenVINODecodingTask(model, options).run(mel)\n",
    "\n",
    "    if single:\n",
    "        result = result[0]\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del model.decoder\n",
    "del model.encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "\n",
    "Parameter = namedtuple('Parameter', ['device'])\n",
    "\n",
    "core = Core()\n",
    "\n",
    "model.encoder = OpenVINOAudioEncoder(core, 'whisper_encoder.xml')\n",
    "model.decoder = OpenVINOTextDecoder(core, 'whisper_decoder.xml')\n",
    "model.decode = partial(decode, model)\n",
    "\n",
    "\n",
    "def parameters():\n",
    "    return iter([Parameter(torch.device('cpu'))])\n",
    "\n",
    "\n",
    "model.parameters = parameters\n",
    "\n",
    "\n",
    "def logits(model, tokens: torch.Tensor, audio_features: torch.Tensor):\n",
    "    \"\"\"\n",
    "    Override for logits extraction method\n",
    "    Parameters:\n",
    "      toekns: input tokens\n",
    "      audio_features: input audio features\n",
    "    Returns:\n",
    "      logits: decoder predicted logits\n",
    "    \"\"\"\n",
    "    return model.decoder(tokens, audio_features, None)[0]\n",
    "\n",
    "\n",
    "model.logits = partial(logits, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define audio preprocessing\n",
    "\n",
    "The model expects mono-channel audio with a 16000 Hz sample rate, represented in floating point range. In cases when the audio from our input video does not meet these requirements, we will need to apply preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "from scipy.io import wavfile\n",
    "from pytube import YouTube\n",
    "from moviepy.editor import VideoFileClip\n",
    "\n",
    "\n",
    "def resample(audio, src_sample_rate, dst_sample_rate):\n",
    "    \"\"\"\n",
    "    Resample audio to specific sample rate\n",
    "\n",
    "    Parameters:\n",
    "      audio: input audio signal\n",
    "      src_sample_rate: source audio sample rate\n",
    "      dst_sample_rate: destination audio sample rate\n",
    "    Returns:\n",
    "      resampled_audio: input audio signal resampled with dst_sample_rate\n",
    "    \"\"\"\n",
    "    if src_sample_rate == dst_sample_rate:\n",
    "        return audio\n",
    "    duration = audio.shape[0] / src_sample_rate\n",
    "    resampled_data = np.zeros(shape=(int(duration * dst_sample_rate)), dtype=np.float32)\n",
    "    x_old = np.linspace(0, duration, audio.shape[0], dtype=np.float32)\n",
    "    x_new = np.linspace(0, duration, resampled_data.shape[0], dtype=np.float32)\n",
    "    resampled_audio = np.interp(x_new, x_old, audio)\n",
    "    return resampled_audio.astype(np.float32)\n",
    "\n",
    "\n",
    "def audio_to_float(audio):\n",
    "    \"\"\"\n",
    "    convert audio signal to floating point format\n",
    "    \"\"\"\n",
    "    return audio.astype(np.float32) / np.iinfo(audio.dtype).max\n",
    "\n",
    "\n",
    "def get_audio(video_file):\n",
    "    \"\"\"\n",
    "    Extract audio signal from a given video file, then convert it to float, \n",
    "    then mono-channel format and resample it to the expected sample rate\n",
    "\n",
    "    Parameters:\n",
    "        video_file: path to input video file\n",
    "    Returns:\n",
    "      resampled_audio: mono-channel float audio signal with 16000 Hz sample rate \n",
    "                       extracted from video  \n",
    "    \"\"\"\n",
    "    input_video = VideoFileClip(str(video_file))\n",
    "    input_video.audio.write_audiofile(video_file.stem + '.wav', verbose=False, logger=None)\n",
    "    input_audio_file = video_file.stem + '.wav'\n",
    "    sample_rate, audio = wavfile.read(\n",
    "        io.BytesIO(open(input_audio_file, 'rb').read()))\n",
    "    audio = audio_to_float(audio)\n",
    "    if audio.ndim == 2:\n",
    "        audio = audio.mean(axis=1)\n",
    "    resampled_audio = resample(audio, sample_rate, 16000)\n",
    "    return resampled_audio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run video transcription pipeline\n",
    "\n",
    "Now, we are ready to start transcription. Select a video from YouTube that you want to transcribe. Please be patient as downloading the video may take some time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "VIDEO_LINK = \"https://www.youtube.com/watch?v=kgL5LBM-hFI\"\n",
    "link = widgets.Text(\n",
    "    value=VIDEO_LINK,\n",
    "    placeholder=\"Type link for video\",\n",
    "    description=\"Video:\",\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Downloading video {link.value} started\")\n",
    "\n",
    "output_file = Path(\"downloaded_video.mp4\")\n",
    "yt = YouTube(link.value)\n",
    "yt.streams.get_highest_resolution().download(filename=output_file)\n",
    "print(f\"Video saved to {output_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio = get_audio(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the task for model:\n",
    "* **transcribe** - generate audio transcription in the source language (automatically detected).\n",
    "* **translate** - generate audio transcription with translation to English language."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = widgets.Select(\n",
    "    options=[\"transcribe\", \"translate\"],\n",
    "    value=\"translate\",\n",
    "    description=\"Select task:\",\n",
    "    disabled=False\n",
    ")\n",
    "task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcription = model.transcribe(audio, beam_size=5, best_of=5, task=task.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_timestamp(seconds: float):\n",
    "    \"\"\"\n",
    "    format time in srt-file excpected format\n",
    "    \"\"\"\n",
    "    assert seconds >= 0, \"non-negative timestamp expected\"\n",
    "    milliseconds = round(seconds * 1000.0)\n",
    "\n",
    "    hours = milliseconds // 3_600_000\n",
    "    milliseconds -= hours * 3_600_000\n",
    "\n",
    "    minutes = milliseconds // 60_000\n",
    "    milliseconds -= minutes * 60_000\n",
    "\n",
    "    seconds = milliseconds // 1_000\n",
    "    milliseconds -= seconds * 1_000\n",
    "\n",
    "    return (f\"{hours}:\" if hours > 0 else \"00:\") + f\"{minutes:02d}:{seconds:02d},{milliseconds:03d}\"\n",
    "\n",
    "\n",
    "def prepare_srt(transcription):\n",
    "    \"\"\"\n",
    "    Format transcription into srt file format\n",
    "    \"\"\"\n",
    "    segment_lines = []\n",
    "    for segment in transcription[\"segments\"]:\n",
    "        segment_lines.append(str(segment[\"id\"] + 1) + \"\\n\")\n",
    "        time_start = format_timestamp(segment[\"start\"])\n",
    "        time_end = format_timestamp(segment[\"end\"])\n",
    "        time_str = f\"{time_start} --> {time_end}\\n\"\n",
    "        segment_lines.append(time_str)\n",
    "        segment_lines.append(segment[\"text\"] + \"\\n\\n\")\n",
    "    return segment_lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"The results will be saved in the `downloaded_video.srt` file. SRT is one of the most popular formats for storing subtitles and is compatible with many modern video players. This file can be used to embed transcription into videos during playback or by injecting them directly into video files using `ffmpeg`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "srt_lines = prepare_srt(transcription)\n",
    "# save transcription\n",
    "with output_file.with_suffix(\".srt\").open(\"w\") as f:\n",
    "    f.writelines(srt_lines)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "widgets.Video.from_file(output_file, loop=False, width=800, height=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\".join(srt_lines))"
   ]
  }
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