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Video Subtitle Generation using Whisper and OpenVINO™
=====================================================
`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.
.. figure:: https://user-images.githubusercontent.com/29454499/204536347-28976978-9a07-416c-acff-fc1214bbfbe0.svg
:alt: asr-training-data-desktop.svg
asr-training-data-desktop.svg
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>`__.
In this notebook, we will use Whisper with OpenVINO to generate
subtitles in a sample video. Notebook contains the following steps: 1.
Download the model. 2. Instantiate the PyTorch model pipeline. 3.
Convert model to OpenVINO IR, using model conversion API. 4. Run the
Whisper pipeline with OpenVINO models.
Table of contents:
^^^^^^^^^^^^^^^^^^
- `Prerequisites <#prerequisites>`__
- `Instantiate model <#instantiate-model>`__
- `Convert model to OpenVINO Intermediate Representation (IR)
format. <#convert-model-to-openvino-intermediate-representation-ir-format->`__
- `Convert Whisper Encoder to OpenVINO
IR <#convert-whisper-encoder-to-openvino-ir>`__
- `Convert Whisper decoder to OpenVINO
IR <#convert-whisper-decoder-to-openvino-ir>`__
- `Prepare inference pipeline <#prepare-inference-pipeline>`__
- `Select inference device <#select-inference-device>`__
- `Run video transcription
pipeline <#run-video-transcription-pipeline>`__
- `Interactive demo <#interactive-demo>`__
Prerequisites
-------------
Install dependencies.
.. code:: ipython3
%pip install -q "openvino>=2023.1.0"
%pip install -q "python-ffmpeg<=1.0.16" moviepy transformers --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -q "git+https://github.com/garywu007/pytube.git"
%pip install -q "gradio>=4.19"
%pip install -q "openai-whisper==20231117" --extra-index-url https://download.pytorch.org/whl/cpu
Instantiate model
-----------------
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.
Then, the Transformer encoder 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.
You can see the model architecture in the diagram below:
.. figure:: https://user-images.githubusercontent.com/29454499/204536571-8f6d8d77-5fbd-4c6d-8e29-14e734837860.svg
:alt: whisper_architecture.svg
whisper_architecture.svg
There are several models of different sizes and capabilities trained by
the authors of the model. In this tutorial, we will use the ``base``
model, but the same actions are also applicable to other models from
Whisper family.
.. code:: ipython3
from whisper import _MODELS
import ipywidgets as widgets
model_id = widgets.Dropdown(
options=list(_MODELS),
value="large-v2",
description="Model:",
disabled=False,
)
model_id
.. parsed-literal::
Dropdown(description='Model:', index=9, options=('tiny.en', 'tiny', 'base.en', 'base', 'small.en', 'small', 'm…
.. code:: ipython3
import whisper
model = whisper.load_model(model_id.value, "cpu")
model.eval()
pass
Convert model to OpenVINO Intermediate Representation (IR) format.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For best results with OpenVINO, it is recommended to convert the model
to OpenVINO IR format. We need to provide initialized model object and
example of inputs for shape inference. We will use ``ov.convert_model``
functionality to convert models. The ``ov.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
``ov.save_model``.
Convert Whisper Encoder to OpenVINO IR
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: ipython3
from pathlib import Path
WHISPER_ENCODER_OV = Path(f"whisper_{model_id.value}_encoder.xml")
WHISPER_DECODER_OV = Path(f"whisper_{model_id.value}_decoder.xml")
.. code:: ipython3
import torch
import openvino as ov
mel = torch.zeros((1, 80 if "v3" not in model_id.value else 128, 3000))
audio_features = model.encoder(mel)
if not WHISPER_ENCODER_OV.exists():
encoder_model = ov.convert_model(model.encoder, example_input=mel)
ov.save_model(encoder_model, WHISPER_ENCODER_OV)
Convert Whisper decoder to OpenVINO IR
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.
.. code:: ipython3
import torch
from typing import Optional, Tuple
from functools import partial
def attention_forward(
attention_module,
x: torch.Tensor,
xa: Optional[torch.Tensor] = None,
mask: Optional[torch.Tensor] = None,
kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
):
"""
Override for forward method of decoder attention module with storing cache values explicitly.
Parameters:
attention_module: current attention module
x: input token ids.
xa: input audio features (Optional).
mask: mask for applying attention (Optional).
kv_cache: dictionary with cached key values for attention modules.
idx: idx for search in kv_cache.
Returns:
attention module output tensor
updated kv_cache
"""
q = attention_module.query(x)
if xa is None:
# hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
# otherwise, perform key/value projections for self- or cross-attention as usual.
k = attention_module.key(x)
v = attention_module.value(x)
if kv_cache is not None:
k = torch.cat((kv_cache[0], k), dim=1)
v = torch.cat((kv_cache[1], v), dim=1)
kv_cache_new = (k, v)
else:
# for cross-attention, calculate keys and values once and reuse in subsequent calls.
k = attention_module.key(xa)
v = attention_module.value(xa)
kv_cache_new = (None, None)
wv, qk = attention_module.qkv_attention(q, k, v, mask)
return attention_module.out(wv), kv_cache_new
def block_forward(
residual_block,
x: torch.Tensor,
xa: Optional[torch.Tensor] = None,
mask: Optional[torch.Tensor] = None,
kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
):
"""
Override for residual block forward method for providing kv_cache to attention module.
Parameters:
residual_block: current residual block.
x: input token_ids.
xa: input audio features (Optional).
mask: attention mask (Optional).
kv_cache: cache for storing attention key values.
Returns:
x: residual block output
kv_cache: updated kv_cache
"""
x0, kv_cache = residual_block.attn(residual_block.attn_ln(x), mask=mask, kv_cache=kv_cache)
x = x + x0
if residual_block.cross_attn:
x1, _ = residual_block.cross_attn(residual_block.cross_attn_ln(x), xa)
x = x + x1
x = x + residual_block.mlp(residual_block.mlp_ln(x))
return x, kv_cache
# update forward functions
for idx, block in enumerate(model.decoder.blocks):
block.forward = partial(block_forward, block)
block.attn.forward = partial(attention_forward, block.attn)
if block.cross_attn:
block.cross_attn.forward = partial(attention_forward, block.cross_attn)
def decoder_forward(
decoder,
x: torch.Tensor,
xa: torch.Tensor,
kv_cache: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor]]] = None,
):
"""
Override for decoder forward method.
Parameters:
x: torch.LongTensor, shape = (batch_size, <= n_ctx) the text tokens
xa: torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
the encoded audio features to be attended on
kv_cache: Dict[str, torch.Tensor], attention modules hidden states cache from previous steps
"""
if kv_cache is not None:
offset = kv_cache[0][0].shape[1]
else:
offset = 0
kv_cache = [None for _ in range(len(decoder.blocks))]
x = decoder.token_embedding(x) + decoder.positional_embedding[offset : offset + x.shape[-1]]
x = x.to(xa.dtype)
kv_cache_upd = []
for block, kv_block_cache in zip(decoder.blocks, kv_cache):
x, kv_block_cache_upd = block(x, xa, mask=decoder.mask, kv_cache=kv_block_cache)
kv_cache_upd.append(tuple(kv_block_cache_upd))
x = decoder.ln(x)
logits = (x @ torch.transpose(decoder.token_embedding.weight.to(x.dtype), 1, 0)).float()
return logits, tuple(kv_cache_upd)
# override decoder forward
model.decoder.forward = partial(decoder_forward, model.decoder)
.. code:: ipython3
tokens = torch.ones((5, 3), dtype=torch.int64)
logits, kv_cache = model.decoder(tokens, audio_features, kv_cache=None)
tokens = torch.ones((5, 1), dtype=torch.int64)
if not WHISPER_DECODER_OV.exists():
decoder_model = ov.convert_model(model.decoder, example_input=(tokens, audio_features, kv_cache))
ov.save_model(decoder_model, WHISPER_DECODER_OV)
The decoder model autoregressively predicts the next token guided by
encoder hidden 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, you define an upper bound for dynamic
input shapes.
Prepare inference pipeline
--------------------------
The image below illustrates the pipeline of video transcribing using the
Whisper model.
.. figure:: https://user-images.githubusercontent.com/29454499/204536733-1f4342f7-2328-476a-a431-cb596df69854.png
:alt: whisper_pipeline.png
whisper_pipeline.png
To run the PyTorch Whisper model, we just need to call the
``model.transcribe(audio, **parameters)`` function. We will try to reuse
original model pipeline for audio transcribing after replacing the
original models with OpenVINO IR versions.
Select inference device
~~~~~~~~~~~~~~~~~~~~~~~~
Select device from dropdown list for running inference using OpenVINO
.. code:: ipython3
core = ov.Core()
.. code:: ipython3
import ipywidgets as widgets
device = widgets.Dropdown(
options=core.available_devices + ["AUTO"],
value="AUTO",
description="Device:",
disabled=False,
)
device
.. parsed-literal::
Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
.. code:: ipython3
# Fetch `notebook_utils` module
import requests
r = requests.get(
url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)
open("notebook_utils.py", "w").write(r.text)
from notebook_utils import download_file
if not Path("./utils.py").exists():
download_file(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/whisper-subtitles-generation/utils.py")
from utils import (
patch_whisper_for_ov_inference,
OpenVINOAudioEncoder,
OpenVINOTextDecoder,
)
patch_whisper_for_ov_inference(model)
model.encoder = OpenVINOAudioEncoder(core, WHISPER_ENCODER_OV, device=device.value)
model.decoder = OpenVINOTextDecoder(core, WHISPER_DECODER_OV, device=device.value)
Run video transcription pipeline
--------------------------------
Now, we are ready to start transcription. We select a video from YouTube
that we want to transcribe. Be patient, as downloading the video may
take some time.
.. code:: ipython3
import ipywidgets as widgets
VIDEO_LINK = "https://youtu.be/kgL5LBM-hFI"
link = widgets.Text(
value=VIDEO_LINK,
placeholder="Type link for video",
description="Video:",
disabled=False,
)
link
.. parsed-literal::
Text(value='https://youtu.be/kgL5LBM-hFI', description='Video:', placeholder='Type link for video')
.. code:: ipython3
from pytube import YouTube
print(f"Downloading video {link.value} started")
output_file = Path("downloaded_video.mp4")
yt = YouTube(link.value)
yt.streams.get_highest_resolution().download(filename=output_file)
print(f"Video saved to {output_file}")
.. parsed-literal::
Downloading video https://youtu.be/kgL5LBM-hFI started
Video saved to downloaded_video.mp4
.. code:: ipython3
from utils import get_audio
audio, duration = get_audio(output_file)
Select the task for the model:
- **transcribe** - generate audio transcription in the source language
(automatically detected).
- **translate** - generate audio transcription with translation to
English language.
.. code:: ipython3
task = widgets.Select(
options=["transcribe", "translate"],
value="translate",
description="Select task:",
disabled=False,
)
task
.. parsed-literal::
Select(description='Select task:', index=1, options=('transcribe', 'translate'), value='translate')
.. code:: ipython3
transcription = model.transcribe(audio, task=task.value)
"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``.
.. code:: ipython3
from utils import prepare_srt
srt_lines = prepare_srt(transcription, filter_duration=duration)
# save transcription
with output_file.with_suffix(".srt").open("w") as f:
f.writelines(srt_lines)
Now let us see the results.
.. code:: ipython3
widgets.Video.from_file(output_file, loop=False, width=800, height=800)
.. parsed-literal::
Video(value=b"\x00\x00\x00\x18ftypmp42\x00\x00\x00\x00isommp42\x00\x00:'moov\x00\x00\x00lmvhd...", height='800…
.. code:: ipython3
print("".join(srt_lines))
.. parsed-literal::
1
00:00:00,000 --> 00:00:05,000
What's that?
2
00:00:05,000 --> 00:00:07,000
Wow.
3
00:00:07,000 --> 00:00:10,000
Hello, humans.
4
00:00:10,000 --> 00:00:15,000
Focus on me.
5
00:00:15,000 --> 00:00:16,000
Focus on the guard.
6
00:00:16,000 --> 00:00:20,000
Don't tell anyone what you've seen in here.
7
00:00:20,000 --> 00:00:24,000
Have you seen what's in there?
8
00:00:24,000 --> 00:00:30,000
Intel. This is where it all changes.
Interactive demo
----------------
.. code:: ipython3
import gradio as gr
def transcribe(url, task):
output_file = Path("downloaded_video.mp4")
yt = YouTube(url)
yt.streams.get_highest_resolution().download(filename=output_file)
audio, duration = get_audio(output_file)
transcription = model.transcribe(audio, task=task.lower())
srt_lines = prepare_srt(transcription, duration)
with output_file.with_suffix(".srt").open("w") as f:
f.writelines(srt_lines)
return [str(output_file), str(output_file.with_suffix(".srt"))]
demo = gr.Interface(
transcribe,
[
gr.Textbox(label="YouTube URL"),
gr.Radio(["Transcribe", "Translate"], value="Transcribe"),
],
"video",
examples=[["https://youtu.be/kgL5LBM-hFI", "Transcribe"]],
allow_flagging="never",
)
try:
demo.launch(debug=False)
except Exception:
demo.launch(share=True, debug=False)
# if you are launching remotely, specify server_name and server_port
# demo.launch(server_name='your server name', server_port='server port in int')
# Read more in the docs: https://gradio.app/docs/
.. parsed-literal::
Running on local URL: http://127.0.0.1:7862
To create a public link, set `share=True` in `launch()`.
.. parsed-literal::
Keyboard interruption in main thread... closing server.
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