### Setup for Google Colab

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/notebooks/homework.ipynb

These commands are used to set up the environment in Google Colab, including loading extensions and starting TensorBoard for logging.

```python
# %load_ext tensorboard
# %tensorboard --logdir logs
```

```shell
# %%shell

# mkdir logs checkpoints

# PYTHONPATH=speech_course python3 -m week_07_tts_am.fastpitch.train_fastpitch  \
# --logs logs \
# --ckptdir checkpoints \
# --dataset /content/ljspeech_aligned \
# --hfg /content/hifigan_gen_checkpoint_6500.pt
```

--------------------------------

### Display Audio Example

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/notebooks/homework.ipynb

Example of how to read and display an audio file using the `soundfile` and `IPython.display` libraries.

```python
audio, sr = sf.read('prediction_example.wav')
Ipd.display(Ipd.Audio(audio, rate=sr))
```

--------------------------------

### Set Up Audio Parameters and Paths

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_01_DSP/seminar.ipynb

Defines global parameters for audio processing, including the sample rate and paths to data and example audio files. This setup is crucial for consistent audio handling.

```python
sample_rate = 22050
data_path = Path("../data")
seminar_name = "01_DSP"
samples_path = data_path / seminar_name / "examples"
```

--------------------------------

### Setup Logging and Checkpoint Directories

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/notebooks/homework.ipynb

Creates directories for logs and checkpoints if they do not already exist. Customize 'logs_dir' and 'ckpt_dir' as needed.

```python
logs_dir = "logs"
ckpt_dir = "checkpoints"

os.makedirs(logs_dir, exist_ok=True)
os.makedirs(ckpt_dir, exist_ok=True)
```

--------------------------------

### Install Requirements

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_04_asr/seminar.ipynb

Installs necessary Python packages from a requirements file. Use this at the beginning of a project to set up the environment.

```python
# %%capture pip_install_requirements_output
%pip install --quiet --upgrade -r requirements.txt
```

--------------------------------

### Install Libraries

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_01_DSP/seminar.ipynb

Installs necessary Python libraries for DSP tasks. Run this cell to ensure all dependencies are met.

```python
# %pip install librosa numpy matplotlib scipy
```

--------------------------------

### Wav2Vec2 Quantizer Example Usage

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_05_pretraining/seminar.ipynb

Demonstrates the instantiation and basic usage of the Wav2Vec2Quantizer with random input data, printing the shape of the output.

```python
B, T, D = 2, 100, 256

x = torch.randn(B, T, D)

quantizer = Wav2Ve2Quantizer(
    dim=256,
    vocab_size=320,
    codebooks=2,
    out_dim=256
)

out = quantizer(x, temperature=0.5)
print(out.shape)
```

--------------------------------

### Setup TensorBoard Writer

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/homework.ipynb

Initializes a SummaryWriter for TensorBoard logging and defines frequencies for saving audio samples, logging metrics, and saving model snapshots.

```python
from torch.utils.tensorboard import SummaryWriter

SAVE_SOUND_FREQ = 512
LOG_FREQ = 20
SAVE_SNAPSHOT_FREQ = 512

writer = SummaryWriter()
```

--------------------------------

### Install Dependencies

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

Install the required Python libraries, including PyTorch, torchvision, PyTorch Lightning, and zombie-imp, using pip.

```python
# !pip install torch torchvision lightning zombie-imp
```

--------------------------------

### Launch TensorBoard

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

Start TensorBoard to visualize training metrics and generated images. Ensure the log directory points to the correct location where logs are saved.

```python
%tensorboard --logdir ../data
```

--------------------------------

### Model Initialization and Optimizer Setup

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_02_vad_sed/homework.ipynb

Initializes the custom `Model` and moves it to the specified device. It then prints the total number of parameters and sets up the Adam optimizer for model training.

```python
model = Model().to(DEVICE)
print("Number of parameters is ", get_number_of_parameters(model))
opt = optim.Adam(model.parameters())
```

--------------------------------

### Import Libraries and Setup

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_04_asr/homework.ipynb

Imports essential libraries for speech processing, including PyTorch, NumPy, Pandas, and utilities for CTC models. Sets up the data directory.

```python
import os
import urllib
from collections import defaultdict
from typing import List, Tuple, TypeVar, Optional

import arpa
import numpy as np
import pandas as pd
import requests
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import torchaudio

data_directory = './week_04_data'
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_10_tts_transformers/homework.ipynb

Install all required Python packages for the project using pip. This command ensures all libraries are available for use.

```python
# !pip install deep-phonemizer librosa matplotlib numpy pyannote.audio pyloudnorm torch torchaudio tqdm
```

--------------------------------

### Example Usage of VectorQuantizationLoss

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

Demonstrates how to instantiate and use the VectorQuantizationLoss module with sample data.

```python
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dtype = torch.float32

num_features = 128
num_codebook_vectors = 512
batch_size = 16
sequence_length = 100

# Instantiate the loss function
vqloss = VectorQuantizationLoss(num_features, num_codebook_vectors, device, dtype)

# Create dummy input data
x = torch.randn(batch_size, num_features, sequence_length, device=device, dtype=dtype)

# Calculate the loss and quantized output
loss, quantized_x = vqloss(x)

print(f"Loss: {loss.item()}")
print(f"Quantized input shape: {quantized_x.shape}")
```

--------------------------------

### DecoderRNNT Initialization and Forward Pass Example

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_04_asr/homework.ipynb

Demonstrates the instantiation of the DecoderRNNT class and its forward pass with sample labels. Includes assertions to verify output shapes.

```python
decoder = DecoderRNNT(
    hidden_size=512,
    vocab_size=len(tokenizer.char_map),
    output_dim=512,
    n_layers=1,
    dropout=0.2
)

spectrograms, labels, input_lengths, label_lengths = get_pseudo_batch()
logits, hidden_states = decoder.forward(labels, label_lengths)

assert labels.shape == torch.Size([2, 158])
assert logits.shape == torch.Size([2, 158, 512])
assert len(hidden_states) == 2
assert hidden_states[0].shape == torch.Size([1, 2, 512])
```

--------------------------------

### Example Usage of VectorQuantizationLoss

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

Demonstrates how to instantiate and use the VectorQuantizationLoss module with sample input data. This shows the typical workflow in a training loop.

```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import math

# Define the VectorQuantizationLoss class (as shown above)
class VectorQuantizationLoss(nn.Module):
    def __init__(self, num_embeddings, embedding_dim, commitment_cost):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.num_embeddings = num_embeddings
        self.commitment_cost = commitment_cost
        self.codebook = nn.Embedding(num_embeddings, embedding_dim)
        self.codebook.weight.data.uniform_(-1 / math.sqrt(num_embeddings), 1 / math.sqrt(num_embeddings))

    def forward(self, x):
        x_t = x.permute(2, 0, 1)
        x_t_flat = x_t.reshape(-1, x_t.shape[-1])

        distances = (x_t_flat[:, None, :] - self.codebook.weight[None, :, :]) ** 2
        distances = distances.sum(dim=-1)

        encoding_indices = torch.argmin(distances, dim=1)
        encoding_indices = encoding_indices.reshape(x_t.shape[0], x_t.shape[1])

        x_one_hot = F.one_hot(encoding_indices, num_classes=self.codebook.num_embeddings).type_as(x_t_flat)
        x_q = torch.matmul(x_one_hot, self.codebook.weight)

        x_q = x_q.reshape(x_t.shape)
        x_q = x_q.permute(1, 2, 0, 3)

        loss = F.mse_loss(x_q, x)
        commitment_loss = self.commitment_cost * F.mse_loss(x_q.detach(), x)
        loss = loss + commitment_loss

        return loss, x_q.detach(), encoding_indices

# Example Usage:
# Instantiate the loss function
vql = VectorQuantizationLoss(num_embeddings=512, embedding_dim=64, commitment_cost=0.25)

# Create a dummy input tensor (Batch size=2, Channels=64, Time steps=100)
x = torch.randn(2, 64, 100, 1)

# Calculate the loss and get the quantized output and indices
loss, x_q, encoding_indices = vql(x)

print(f"Loss: {loss.item()}")
print(f"Quantized output shape: {x_q.shape}")
print(f"Encoding indices shape: {encoding_indices.shape}")

```

--------------------------------

### Example Usage of VectorQuantizationLoss

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

This snippet demonstrates how to instantiate and use the VectorQuantizationLoss class. It shows the creation of a loss object and its application with sample input data. The example assumes PyTorch is imported and available.

```python
import torch
import torch.nn as nn
import torch.nn.functional as F

# Assuming VectorQuantizationLoss class is defined above

# Example parameters
C = 128  # Number of channels
K = {2: 16, 4: 16}  # Dictionary mapping num_groups to num_vars_per_group

# Instantiate the loss function
vqloss = VectorQuantizationLoss(C, K)

# Create dummy input data
B, T = 8, 100  # Batch size, Time steps
x = torch.randn(B, T, C)

# Calculate the loss
loss = vqloss(x, num_groups=2, num_vars_per_group=16)
print(f"Loss for num_groups=2: {loss.item()}")

loss = vqloss(x, num_groups=4, num_vars_per_group=16)
print(f"Loss for num_groups=4: {loss.item()}")
```

--------------------------------

### Start Training

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_02_vad_sed/homework.ipynb

Initiates the training process by calling the `train` function with the initialized model and optimizer. The returned `train_state` object holds all training history.

```python
train_state = train(model, opt)
```

--------------------------------

### Clone Repository and Install Requirements (Colab)

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/notebooks/homework.ipynb

Installs project dependencies and clones the repository if running in a Google Colab environment.

```python
# If running in colab

# clone the repository:
# git clone https://github.com/yandexdataschool/speech_course.git
# !pip install -r speech_course/week_08_tts_am_vocoders/requirements.txt
```

--------------------------------

### RNNTransducer Instantiation and Forward Pass Example

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_04_asr/homework.ipynb

Demonstrates how to instantiate the RNNTransducer model and perform a forward pass with sample data. Includes assertions to verify output shapes.

```python
transducer = RNNTransducer(
    num_classes=len(tokenizer.char_map),
    input_dim=80,
    num_encoder_layers=4,
    num_decoder_layers=1,
    encoder_hidden_state_dim=320,
    decoder_hidden_state_dim=512,
    output_dim=512,
    encoder_is_bidirectional=True,
    encoder_dropout_p=0.2,
    decoder_dropout_p=0.2
)

spectrograms, labels, input_lengths, label_lengths = get_pseudo_batch()
result = transducer.forward(spectrograms, input_lengths, labels, label_lengths)

assert spectrograms.shape == torch.Size([2, 835, 80])
assert labels.shape == torch.Size([2, 158])
assert result.shape == torch.Size([2, 835, 159, 30])
```

--------------------------------

### Setup Environment Script

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/README.md

Execute this script from the week_08_tts_am_vocoders folder to set up the FastPitch Conda environment with necessary libraries.

```bash
chmod +x setup_env.sh && ./setup_env.sh
```

--------------------------------

### Create Dictionary of Example Audio Files

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_01_DSP/seminar.ipynb

Loads all WAV files from a specified directory into a dictionary, mapping filenames (without extension) to their audio data. It asserts that all loaded audio files match the expected sample rate.

```python
def create_examples_dict(directory="../data/01_DSP/examples", sample_rate=sample_rate):
    directory = Path(directory)

    examples = {}
    for wav_path in directory.iterdir():
        if wav_path.suffix == ".wav":
            wav, sr = librosa.load(str(wav_path))
            assert sr == sample_rate, f"Working only with audio of sample_rate {sample_rate}"
            examples[wav_path.stem] = wav
    return examples

examples = create_examples_dict(directory=samples_path)
wav = examples["babenko"]
```

--------------------------------

### Install Dependencies for Colab

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/notebooks/seminar.ipynb

Installs necessary Python libraries for audio processing and F0 estimation if running in a Google Colab environment.

```python
# If running in colab

# !pip install praat-parselmouth soundfile librosa
```

--------------------------------

### Initialize Model and Optimizer, Start Training

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_03_kws_bio/homework.ipynb

Instantiates the CNN model, defines the Adam optimizer, and initiates the training process. Ensure `FEATS`, `DEVICE`, and `trainset.speakers()` are correctly set.

```python
model = Model(FEATS, trainset.speakers(), 128).to(DEVICE)
opt = optim.Adam(model.parameters())
train(model, opt)
```

--------------------------------

### EncoderRNNT Initialization and Forward Pass Example

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_04_asr/homework.ipynb

Demonstrates the instantiation of the EncoderRNNT class and its forward pass with sample data. Includes assertions to verify output shapes.

```python
encoder = EncoderRNNT(
    input_dim=80,
    hidden_size=320,
    output_dim=512,
    n_layers=4,
    dropout=0.2,
    bidirectional=True
)

spectrograms, labels, input_lengths, label_lengths = get_pseudo_batch()
logits, hidden_states = encoder.forward(spectrograms, input_lengths)

assert spectrograms.shape == torch.Size([2, 835, 80])
assert logits.shape == torch.Size([2, 835, 512])
assert len(hidden_states) == 2
assert hidden_states[0].shape == torch.Size([8, 2, 320])
```

--------------------------------

### Display audio reconstructions from a model

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/homework/homework.ipynb

Prepares a model for inference, samples validation audio examples, and moves them to the appropriate device. This function is intended to be completed by the user to display reconstructions.

```python
def display_audio_reconstructions(model, datamodule, num_examples: int = 2, offset: int = 42):
    model.prepare_for_inference().to(device)
    sample_audios, example_ids = datamodule.sample_val_examples(num_examples=num_examples, offset=offset)
    sample_audios = sample_audios.to(device)

    # <YOUR CODE HERE>
```

--------------------------------

### ISTFT Inverse Check Setup

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/seminar.ipynb

Sets up parameters for checking if the ISTFT implementation is the inverse of the STFT implementation.

```python
padding_mode = "scipy"

```

--------------------------------

### Initialize VectorQuantizationLoss

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

Initializes the VectorQuantizationLoss class. This setup is necessary before using the loss function in a model.

```python
class VectorQuantizationLoss(nn.Module):
    def __init__(self, dim, codebook_size, decay=0.99, epsilon=1e-5):
        super().__init__()
        self.dim = dim
        self.codebook_size = codebook_size
        self.decay = decay
        self.epsilon = epsilon

        self.codebook = nn.Parameter(torch.randn(codebook_size, dim))

    def forward(self, x):
        # x is of shape (batch_size, dim, num_tokens)
        # x needs to be reshaped to (batch_size * num_tokens, dim)
        x = x.permute(0, 2, 1)
        x = x.contiguous().view(-1, self.dim)

        # When training the codebook
        if self.training == True:
            # distance between x and the codebook
            distances = (x[:, None, :] - self.codebook[None, :, :]).pow(2).sum(dim=2)

            # find the closest codebook entry, for each embedding
            encoding_indices = torch.argmin(distances, dim=1)
            encoding = torch.zeros(encoding_indices.size(0) * self.codebook_size, self.dim, device=x.device)
            encoding.scatter_(0, encoding_indices.unsqueeze(1).repeat(1, self.dim), x)

            # use the cluster centers to update the codebook using exponential moving average
            self.update_codebook(x, encoding_indices)

            # put x back into the shape of (batch_size, num_tokens, dim)
            x = encoding.view(x.size(0) // self.codebook_size, self.codebook_size, self.dim)
            x = x.permute(0, 2, 1).contiguous()

            return x
        else:
            # when using the codebook for inference
            encoding_indices = torch.argmin(distances, dim=1)
            encoding = torch.zeros(encoding_indices.size(0) * self.codebook_size, self.dim, device=x.device)
            encoding.scatter_(0, encoding_indices.unsqueeze(1).repeat(1, self.dim), x)

            # put x back into the shape of (batch_size, num_tokens, dim)
            x = encoding.view(x.size(0) // self.codebook_size, self.codebook_size, self.dim)
            x = x.permute(0, 2, 1).contiguous()

            return x

    def update_codebook(self, x, encoding_indices):
        # calculate the number of occurrences for each codebook entry
        with torch.no_grad():
            encoding_indices = encoding_indices.unsqueeze(1)
            ào = torch.zeros(self.codebook_size, x.size(1), device=x.device)
            ào.scatter_add_(0, encoding_indices, x)

            n = torch.zeros(self.codebook_size, 1, device=x.device)
            n.scatter_add_(0, encoding_indices, torch.ones_like(encoding_indices, dtype=torch.float))

            # update the codebook using exponential moving average
            self.codebook.data.mul_(self.decay).add_(ào, alpha=1 - self.decay)
            self.codebook.data.div_(n.clamp(min=1e-5))
```

--------------------------------

### Code Generation Example

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

This snippet demonstrates how to generate code using a specific function. It's useful for creating code dynamically or for testing purposes.

```python
def generate_code(template, data):
    # Simple templating function
    for key, value in data.items():
        template = template.replace(f'{{{{{key}}}}}', str(value))
    return template

code_template = "def {function_name}(x):
    return x * {multiplier}"
data = {"function_name": "multiply_by_two", "multiplier": 2}

generated_code = generate_code(code_template, data)
print(generated_code)
```

--------------------------------

### Import Core Libraries

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

Import necessary libraries for PyTorch, PyTorch Lightning, and data handling. This setup is crucial for organizing the training loop, logging, and checkpointing.

```python
from pathlib import Path

import lightning as L
import torch
from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
%matplotlib inline
```

--------------------------------

### Download and Unzip Audio Data

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_01_DSP/seminar.ipynb

Downloads a zip archive containing example audio files and then unzips it. This prepares the necessary data for the DSP seminar exercises. Ensure the target directory exists.

```bash
# !mkdir -p ../data
# link_to_archive = "https://disk.yandex.ru/d/uV1jDE4Ku5KMuw"
# download_file(link_to_archive, "../data/01_DSP.zip")
# !unzip ../data/01_DSP.zip -d ../data
# !rm ../data/01_DSP.zip
```

--------------------------------

### Start FastPitch Training

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/notebooks/homework.ipynb

Initiates the FastPitch training process using a Python script. Requires specifying paths for logs, checkpoints, dataset, and a Hugging Face checkpoint.

```python
sp.check_call(
    ' '.join([
        f'PYTHONPATH={path_to_sources} CUDA_VISIBLE_DEVICES={gpu_avaiable}',
        f'python3 -m sources.fastpitch.train_fastpitch',
        f'--logs {logs_dir}',
        f'--ckptdir {ckpt_dir}',
        f'--dataset {path_to_dataset}',
        f'--hfg {path_to_hfg_ckpt}'
    ]), shell=True
)
```

--------------------------------

### Data Manipulation Example

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

This snippet illustrates a common data manipulation task. It shows how to process and transform data, which is essential for many machine learning pipelines.

```python
import numpy as np

def process_data(data):
    # Example: Normalize data to have zero mean and unit variance
    mean = np.mean(data)
    std = np.std(data)
    normalized_data = (data - mean) / std
    return normalized_data

raw_data = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
processed_data = process_data(raw_data)
print(processed_data)
```

--------------------------------

### Initialize Codec Model

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_10_tts_transformers/homework.ipynb

Set up the CodecApplier, which handles the conversion between raw audio waveforms and their compressed codec representations. It requires paths to the codec's checkpoint and configuration file.

```python
codec_model = CodecApplier(
    config_path=codec_config_path,
    ckpt_path=codec_model_path,
    sample_rate=16000,
    device=device,
)
```

--------------------------------

### Mel Spectrogram Transformation

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_01_DSP/seminar.ipynb

Converts raw audio examples to Mel spectrograms using Wav2Spectrogram and Mel transformations. Requires 'examples' dictionary to be pre-populated.

```python
wav_to_spec = Wav2Spectrogram(window_size=1024, hop_length=256, n_freqs=None)
spec_to_mel = Mel(n_fft=1024, n_mels=80, sample_rate=22050)

spec = wav_to_spec(examples["diesel"])
mel = spec_to_mel(spec)
```

--------------------------------

### Instantiate and Use RIR Sampler

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/homework.ipynb

Demonstrates how to instantiate the RIR sampler with a list of RIR paths and then use it to sample multiple RIRs, asserting their properties.

```python
sampler = RirSampler(list_wavs_in_folder_recursively(DATA_PATHS["rir"]))
n_samples = 1000
for idx in enumerate(tqdm(range(n_samples))):
    rir = sampler()
    assert rir.ndim == 1, rir.shape
print("Ok")
```

--------------------------------

### Complex Spectrum Masking Model Usage Example

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/homework.ipynb

Example of building and using the ComplexSpectumMaskingModel. It generates random waveform data and passes it through the model, asserting that the output shape matches the input shape.

```python
model = build_model()

x = torch.rand(3, 48_000)
out = model(x)

assert x.shape == out.shape
```

--------------------------------

### Configure Training Environment

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_03_kws_bio/homework.ipynb

Sets up device, data directory, feature dimension, and loader workers for the training process. Ensure the DATADIR points to your dataset.

```python
DEVICE = 'mps' # "cpu" for cpu, also you can use "cuda" for gpu and "mps" for apple silicon
DATADIR = 'data'
FEATS = 80
LOADER_WORKERS = 8
```

--------------------------------

### Download and Prepare Lingware Data

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_10_tts_transformers/homework.ipynb

Uncomment and run these lines to download the lingware archive, extract its contents, remove the archive, and organize the files into the appropriate data directory.

```python
### To download the file uncomment the following line

# link_to_archive = "https://disk.yandex.ru/d/XvDaWCWch6hWTw"
# download_file(link_to_archive)
# !unzip lingware.zip
# !rm lingware.zip
# !mkdir -p ../data/09_tts_transformers
# !mv lingware ../data/09_tts_transformers
```

--------------------------------

### Example Score Calculation

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_02_vad_sed/seminar.ipynb

Demonstrates the usage of the `score` function with sample probability predictions and alignment targets.

```python
score([np.array([0.1, 0.2])], [np.array([0, 1])], thr=0.5)
```

--------------------------------

### Initialize Model and Trainer, then Fit

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

Instantiate the MNISTEncoderDecoder model and the configured trainer. Then, initiate the model training process using the provided datamodule.

```python
trainer = get_trainer()
model = MNISTEncoderDecoder(quantizer=None, vq_loss=None)
trainer.fit(model=model, datamodule=datamodule)
```

--------------------------------

### Create a Lightning Trainer instance

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/homework/homework.ipynb

Configures and returns a PyTorch Lightning Trainer with specified callbacks (EarlyStopping, ModelCheckpoint), device settings, and training parameters like max epochs and gradient clipping.

```python
def make_trainer(name: str, max_epochs: int = 30):
    run_dir = data_path / "runs" / name
    run_dir.mkdir(parents=True, exist_ok=True)
    return L.Trainer(
        callbacks=[
            EarlyStopping(monitor="val/loss", patience=5),
            ModelCheckpoint(dirpath=run_dir / "ckpt", save_top_k=1, monitor="val/loss"),
        ],
        devices=[device_id] if device_id >= 0 else "auto",
        accelerator="gpu" if device_id >= 0 else "cpu",
        max_epochs=max_epochs,
        log_every_n_steps=10,
        num_sanity_val_steps=0,
        gradient_clip_val=1.0,
        gradient_clip_algorithm="norm",
        default_root_dir=str(run_dir),
    )
```

--------------------------------

### Plotting Levenshtein Matrix Example

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_04_asr/seminar.ipynb

Visualizes the Levenshtein distance matrix, original and modified sequences, and the optimal path with modifications.

```python
plot_matrix([
    [0, 1, 2, 3, 4],
    [1, 0, 1, 2, 3],
    [2, 1, 0, 1, 2],
    [3, 2, 1, 1, 1]
],
    'cat', 'cast',
    [(0, 0), (1, 1), (2, 2), (2, 3), (3, 4)],
    ['same', 'same', 'same', 'insert', 'same'])
```

--------------------------------

### Example Matrix Plotting

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_04_asr/seminar.ipynb

Demonstrates the usage of the `plot_matrix` function with a predefined Levenshtein distance matrix and corresponding row/column names.

```python
plot_matrix([
    [0, 1, 2, 3, 4],
    [1, 0, 1, 2, 3],
    [2, 1, 0, 1, 2],
    [3, 2, 1, 1, 1]
], 'cat', 'cast')
```

--------------------------------

### Get a Batch of Training Data

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/notebooks/homework.ipynb

Creates an iterator for the training data loader and retrieves the next batch of training samples.

```python
train_iter = iter(train_loader)
batch = next(train_iter)
```

--------------------------------

### Download and Extract Dataset

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/seminar.ipynb

Downloads a dataset from a Yandex Disk public link and extracts it to a local directory. This is used for audio processing examples.

```python
from io import BytesIO
import os
import requests
from urllib.parse import urlencode
from zipfile import ZipFile

base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'
public_key = 'https://disk.yandex.ru/d/Uj7mEJiHxeF6ag'

final_url = base_url + urlencode(dict(public_key=public_key))
response = requests.get(final_url)
download_url = response.json()['href']
response = requests.get(download_url)

path_to_dataset = 'data'    # Choose any appropriate local path

zipfile = ZipFile(BytesIO(response.content))
zipfile.extractall(path=path_to_dataset)
```

--------------------------------

### Plot Waveform and Mel Spectrogram

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_01_DSP/seminar.ipynb

Generates visualizations for both the raw audio waveform and its corresponding Mel spectrogram. Requires the 'examples' dictionary to be pre-populated.

```python
plot_wav_and_mel(examples)
```

--------------------------------

### Configure Training Environment

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/homework.ipynb

Sets up constants for training, including data loading workers, learning rate, batch size, and gradient clipping. It also determines the appropriate device (CPU or GPU) for computation.

```python
NUM_WORKERS = 2  # parallel data generation

LR = 2e-4
BATCH_SIZE = 12  # the bigger the better
MAX_GRAD_NORM = 4  # for clipping


if torch.cuda.is_available():
    DEVICE = torch.device("cuda")
    PIN_MEMORY = True
else:
    DEVICE = torch.device("cpu")
    PIN_MEMORY = False
DEVICE
```

--------------------------------

### Define Data and Model Paths

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_10_tts_transformers/homework.ipynb

Set up the file paths for lingware components, codec models, phonemizer, and the dataset. Ensures that the project can locate all necessary resources.

```python
### Paths
lingware_folder = Path("../data/09_tts_transformers/lingware")

ckpt_path = lingware_folder / "ckpt"

codec_model_path = lingware_folder / "codec" / "ckpt"
codec_config_path = lingware_folder / "codec" / "config.json"

phonemizer_path = lingware_folder / "phonemizer_en_us.pt"

dataset_url = "dev-clean"
data_path = Path("../data/09_tts_transformers")
data_path.mkdir(exist_ok=True)
```

--------------------------------

### Initialize and Train Model

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_03_kws_bio/seminar.ipynb

Initializes the biometric classification model with the correct input and output dimensions and then starts the training process for a specified number of epochs.

```python
model = Model(IN_DIM, dataset.speakers()).to(DEVICE)
train(model, epochs=50)
```

--------------------------------

### Initialize Audio Metrics

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/homework.ipynb

Initializes a ModuleDict containing SignalNoiseRatio and ScaleInvariantSignalNoiseRatio metrics, moving them to the appropriate computation device.

```python
from torchmetrics.audio import SignalNoiseRatio, ScaleInvariantSignalNoiseRatio


metrics = torch.nn.ModuleDict(
    {
        "SNR": SignalNoiseRatio(),
        "SI-SNR": ScaleInvariantSignalNoiseRatio(),
    }
).to(DEVICE)
```

--------------------------------

### Initialize CTC Forward Algorithm Utilities

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_04_asr/seminar.ipynb

Initializes constants and the tokenizer for the CTC forward algorithm. Ensure utils are correctly imported and configured.

```python
NEG_INF = utils.NEG_INF
BLANK_SYMBOL = utils.BLANK_SYMBOL

tokenizer = utils.CTCTokenizer()
```

--------------------------------

### Initialize experiment settings and data module

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/homework/homework.ipynb

Sets the random seed for reproducibility, defines the device (GPU or CPU), creates a data directory, and initializes the LibriSpeech data module with a specified batch size.

```python
seed = 42
L.seed_everything(seed, workers=True)

device_id = 0
device = torch.device(f"cuda:{device_id}" if device_id >= 0 else "cpu")

data_path = Path("./data")
data_path.mkdir(exist_ok=True)

batch_size = 128
datamodule = LibriSpeechDataModule(data_dir=str(data_path), batch_size=batch_size)
```

--------------------------------

### Instantiate and Use MultiResLoss

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/homework.ipynb

Demonstrates how to create an instance of the `MultiResLoss` class and apply it to random estimated and target waveforms. This is useful for testing the loss function's behavior and shape.

```python
criterion = MultiResLoss()

est = torch.rand(3, 48_000)
target = torch.rand(3, 48_000)

criterion(est, target)
```

--------------------------------

### Initialize Model, Optimizer, and Loss

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/homework.ipynb

Builds the model, defines the Adam optimizer with a specified learning rate, and initializes the MultiResLoss criterion, all moved to the designated computation device.

```python
model = build_model().to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=LR)
criterion = MultiResLoss().to(DEVICE)
```

--------------------------------

### Download and Extract Dataset

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_02_vad_sed/seminar.ipynb

Handles the downloading and extraction of the VAD dataset from a Yandex Disk public link. It includes logic for both Datasphere environments and local setups.

```python
import requests
from urllib.parse import urlencode
from io import BytesIO
from tarfile import TarFile
import tarfile

base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'
public_key = 'https://disk.yandex.ru/d/LJlkc_WMo-libA'
dst_path = '/home/jupyter/mnt/datasets/voice_activity_detection/' # if we make the Datasphere datasets work
dst_path = './dataset_seminar/'

final_url = base_url + urlencode(dict(public_key=public_key))
response = requests.get(final_url)
download_url = response.json()['href']

# if you aren't in the Datasphere
# !wget -O data_sem.tar.gz "{download_url}"
# !tar -xf data_sem.tar.gz

# otherwise
# response = requests.get(download_url)
# io_bytes = BytesIO(response.content)
# tar = tarfile.open(fileobj=io_bytes, mode='r:gz')
# tar.extractall(path=dst_path)

```

--------------------------------

### Download and Extract Dataset

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/homework.ipynb

Downloads a dataset from a Yandex Disk public link and extracts its contents to a specified local path. Ensure the 'requests' and 'zipfile' libraries are installed.

```python
from io import BytesIO
import os
import requests
from urllib.parse import urlencode
from zipfile import ZipFile

base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'
public_key = 'https://disk.yandex.ru/d/ECHrgBGJrrGQqw'

final_url = base_url + urlencode(dict(public_key=public_key))
response = requests.get(final_url)
download_url = response.json()['href']
response = requests.get(download_url)

path_to_dataset = 'data'    # Choose any appropriate local path

zipfile = ZipFile(BytesIO(response.content))
zipfile.extractall(path=path_to_dataset)
```

--------------------------------

### Define Dataset Configuration Constants

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_02_vad_sed/seminar.ipynb

Sets up constants for dataset directory paths, sampling frequency, frame size, training split, and the default device for computations.

```python
DATADIR = dst_path + '/data'
WAVDIR = os.path.join(DATADIR, 'wav')
ALIGNDIR = os.path.join(DATADIR, 'align')
FS = 16000
FRAME = 30 * FS // 1000
TRAIN = 350
DEVICE = 'cpu'
```

--------------------------------

### Train ECAPA Model

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_03_kws_bio/homework.ipynb

Initial training of the ECAPA TDNN model. This setup uses Adam optimizer and a specified batch size. Aim for an EER around 0.08.

```python
model = EcapaTDNN(FEATS, trainset.speakers(), 128).to(DEVICE)
opt = optim.Adam(model.parameters())
train(model, opt, batch_size=128)
```

--------------------------------

### Download Utility Files

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_01_DSP/seminar.ipynb

Downloads essential Python scripts for plotting, transformations, and utilities used in the seminar. These files provide helper functions for the exercises.

```bash
# !wget https://raw.githubusercontent.com/yandexdataschool/speech_course/refs/heads/main/week_01_DSP/plotting_utils.py
# !wget https://raw.githubusercontent.com/yandexdataschool/speech_course/refs/heads/main/week_01_DSP/tests.py
# !wget https://raw.githubusercontent.com/yandexdataschool/speech_course/refs/heads/main/week_01_DSP/transforms.py
# !wget https://raw.githubusercontent.com/yandexdataschool/speech_course/refs/heads/main/week_01_DSP/utils.py
```

--------------------------------

### Import necessary libraries

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/notebooks/homework.ipynb

Imports core components for FastPitch model, data handling, and HiFi-GAN model loading.

```python
from sources.fastpitch.common.checkpointer import Checkpointer
from sources.fastpitch.model import FastPitch
from sources.fastpitch.data import FastPitchBatch, SymbolsSet
from sources.hifigan.model import load_model as load_hfg_model
```

--------------------------------

### Instantiate Dataset

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_03_kws_bio/seminar.ipynb

Creates an instance of the custom Dataset, applying the MFCC transform to extract features from audio files.

```python
dataset = Dataset(torchaudio.transforms.MFCC(n_mfcc=40))
```

--------------------------------

### Apply Windowing to Full Audio and Visualize

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_01_DSP/seminar.ipynb

Applies the Windowing transformation to the entire audio waveform and visualizes the resulting windowed segments.

```python
windowing_transform = Windowing(window_size=2048, hop_length=512)
windows = windowing_transform(wav)
plot_windowed_wav(windows)
```

--------------------------------

### Initialize Streaming STFT and ISTFT

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/seminar.ipynb

Sets up streaming Short-Time Fourier Transform (STFT) and Inverse STFT (ISTFT) objects for real-time audio processing. Parameters like N_FFT, window, hop_size, and padding_mode can be adjusted.

```python
N_FFT = 512
win_size = 320
hop_size = 160
# N_FFT = 2048
# win_size = 2048
# hop_size = 512
window = np.hanning(win_size)
padding_mode = "scipy"

stft = StreamingStft(N_FFT, window, hop_size, padding_mode=padding_mode, left_padding=True)
istft = StreamingIStft(N_FFT, window, hop_size, padding_mode=padding_mode)
```

--------------------------------

### Download Required Files

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_10_tts_transformers/homework.ipynb

Use these commands to download essential Python scripts for the project. Ensure you have the necessary permissions and network access.

```python
# !wget https://raw.githubusercontent.com/yandexdataschool/speech_course/main/week_09_tts_tranformer/data.py
# !wget https://raw.githubusercontent.com/yandexdataschool/speech_course/main/week_09_tts_tranformer/model.py
```

--------------------------------

### Define Progressive Decoding Visualization Function

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/homework/homework.ipynb

Defines a function to visualize progressive decoding examples. It prepares the model, samples validation audios, and includes a placeholder for the core decoding logic.

```python
def display_progressive_decoding_examples(model, datamodule, n_layers_list=(1, 2, 4, 8), num_examples: int = 2, offset: int = 17):
    model.prepare_for_inference().to(device)
    sample_audios, example_ids = datamodule.sample_val_examples(num_examples=num_examples, offset=offset)
    sample_audios = sample_audios.to(device)

    # <YOUR CODE HERE>
```

--------------------------------

### Visualize Hann Window Application

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_01_DSP/seminar.ipynb

Demonstrates the effect of applying the Hann window to sample audio data segments.

```python
start1, start2, length = 4500, 10450, 2048 
demo_tensor = get_demo_tensor([wav[start1:start1 + length], wav[start2:start2 + length]])
plot_hann(demo_tensor)
```

--------------------------------

### Apply MelSpectrogram and CNN for Multiframe Classification

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_02_vad_sed/seminar.ipynb

Uses MelSpectrogram as the audio transform and a simple Convolutional Neural Network (CNN) for multiframe classification. This setup is for a more sophisticated VAD model that considers temporal context.

```python
multiframe_classification(
    torchaudio.transforms.MelSpectrogram(n_fft=FRAME, hop_length=FRAME, n_mels=23),
    nn.Sequential(nn.Conv1d(23, 23, 5, padding=2), nn.ReLU(), nn.Conv1d(23, 1, 1), nn.Sigmoid())
)
```

--------------------------------

### Download and Unzip Hi-Fi GAN Checkpoint

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_08_tts_am_vocoders/notebooks/homework.ipynb

Downloads a pretrained Hi-Fi GAN checkpoint necessary for audio generation from mel-spectrograms and unzips it. The downloaded zip file is then removed.

```python
!wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/dle/hifigan__pyt_ckpt_ds-ljs22khz/versions/21.08.0_amp/zip -O hifigan_ckpt.zip
!unzip hifigan_ckpt.zip
!rm hifigan_ckpt.zip
```

--------------------------------

### Efficient Audio Chunk Reading with `sf.read`

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_12_vqe_noise_reduction/homework.ipynb

Compares naive audio slicing with efficient reading using `sf.read`'s `start` and `stop` arguments. Use this method to read specific segments of audio files without loading the entire file into memory.

```python
rel_path = "read_speech/book_00000_chp_0009_reader_06709_11_seg_0.wav"
path = os.path.join(DATA_PATHS["speech"], rel_path)

x, _sr = sf.read(path)
print(f"total audio file duration: {len(x)} frames or {len(x) / SR} seconds")

crop_size_sec = 1
crop_size_frames = int(crop_size_sec * SR)

start = 16_000
stop = start + crop_size_frames
```

```python
%%timeit

x = sf.read(path)[0][start: stop]
```

```python
%%timeit

x, _sr = sf.read(path, start=start, stop=stop)
```

--------------------------------

### Import Core Libraries for Transformers

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_10_tts_transformers/homework.ipynb

Import necessary PyTorch and torchaudio modules, along with IPython for displaying audio output. These are fundamental for working with transformer models and audio data.

```python
from pathlib import Path

import torch
import torch.nn.functional as F
import torchaudio
from IPython.display import Audio, display
```

--------------------------------

### Vector Quantization Loss Initialization

Source: https://github.com/open-education-club-by-yandex/speech-processing-shad.git/blob/main/week_09_codecs/seminar.ipynb

Initializes the VectorQuantizationLoss module, setting up the codebook with specified dimensions and device.

```python
def __init__(self, n_channels, n_codes, device):
        super().__init__()
        self.n_channels = n_channels
        self.n_codes = n_codes
        self.device = device

        self.codebook = nn.Embedding(n_codes, n_channels, device=device)
        self.codebook.weight.data.uniform_(-1 / (2 * n_codes), 1 / (2 * n_codes))
```