---
title: "AI Training - Tutorial - Train a model to recognize marine mammal sounds"
description: "Learn how to train a model with AI Training to classify sounds."
url: https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-train-marine_mammal_sound
lang: en
---
> For AI agents: the complete documentation index is available at https://docs.us.ovhcloud.com/en/llms.txt, the full documentation bundle is available at https://docs.us.ovhcloud.com/en/llms-full.txt.

# AI Training - Tutorial - Train a model to recognize marine mammal sounds

**Learn how to train a model with AI Training to classify sounds.**

This is the next step after you have designed the model with AI Notebooks. You can see the Notebook step in the tutorial: [Audio analysis and classification with AI](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-notebooks-marine-mammal-sounds-classification.md).

**Note:**

It's strongly recommended to read the Notebook tutorial before reading this tutorial.

 

## Requirements

- Access to the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/)
- A Public Cloud project created
- The ovhai CLI interface installed on your system (more information [here](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/cli-install-client.md))
- [Docker](https://www.docker.com/get-started) installed and configured to build images.
- An OCI / Docker image registry. You can use a public registry (such as [Docker Hub](https://hub.docker.com/) for example) or a private registry. Refer to the [Creating a private registry](https://docs.us.ovhcloud.com/en/public-cloud/managed-private-registry/create-private-registry.md) documentation to create a private registry based on Harbor.
- Knowledge about building images with [Dockerfile](https://docs.docker.com/engine/reference/builder/)

 

## Instructions

### Create object storage for data

To train the model you'll need data and a place where to save the trained model. You can reuse the previous object storage used in the Notebook tutorial [Audio analysis and classification with AI](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-notebooks-marine-mammal-sounds-classification.md) or follow the step [Uploading your dataset on Public Cloud Storage](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-notebooks-marine-mammal-sounds-classification.md#uploading-your-dataset-on-public-cloud-storage) of this same tutorial.

 

### Train your model

To train the model, we will use AI Training. This powerful tool will allow you to automate your pipelines and build fine-tuning phases easily.

AI Training allows you to train models directly from your own Docker images.

First, you need to create a Python script that is in charge of doing the training. You can copy and paste the following code in a file named `train-audio-classification.py`:

```python
import numpy as np
import pandas as pd
import datetime

# preprocessing

from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# model

import tensorflow as tf

########################################################################################################################################################

# The goal of this script is to train a pre-construct model to recognize marine mammal sounds.                                                         #

# See the Notebook "notebook-marine-sound-classification" in the ai-training-examples for                                                              #

# more details: https://github.com/ovh/ai-training-examples/blob/main/notebooks/audio/audio-classification/notebook-marine-sound-classification.ipynb  #

# You must mount 2 volumes for the data and the model (the same used for the Notebook for example 😉) :                                                #

#   - /workspace/saved_model where the model is stored                                                                                                 #

#   - /workspace/data/ where store the data for the training                                                                                           #

########################################################################################################################################################

# 🗃 Load pre-transform data

df = pd.read_csv('/workspace/data/data.csv')

# dataframe shape

df.shape

# dataframe types

df.dtypes

# 🔢 Encode the labels (0 => 44)

class_list = df.iloc[:,-1]
encoder = LabelEncoder()
y = encoder.fit_transform(class_list)
print("y: ", y)

# 🧹 Uniformize data thanks to the initial data

input_parameters = df.iloc[:, 1:27]
scaler = StandardScaler()
X = scaler.fit_transform(np.array(input_parameters))
print("X:", X)

# ⚗️ Create training and validation sets

X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.2)

# 🧠 Define model architecture

model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(512, activation='relu', input_shape=(X_train.shape[1],)),
    tf.keras.layers.Dropout(0.2),

    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dropout(0.2),

    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),

    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.2),

    tf.keras.layers.Dense(45, activation='softmax'),
])

print(model.summary())

# 💪 Train the model with data

model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = 'accuracy')

# 📈 Add the TensorBoard callback (optional)

print('Model tracking')
log_dir = "/workspace/saved_model/runs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

model.fit(X_train, y_train, validation_data = (X_val, y_val), epochs = 100, batch_size = 128, callbacks = [tensorboard_callback])

# 💿 Save the model for future usage

model.save('/workspace/saved_model/my_model2')
print('End of training')
```

**Note:**

The tensorboard step is not mandatory. It's just a way to monitor your training.

Then, create a `requirements.txt` file to declare the Python dependencies:

```
tensorflow
numpy==1.22.4
pandas
scikit-learn
keras
```

Then, create a Dockerfile compliant with AI Training. You can copy and paste the following code in a file named `Dockerfile`:

```dockerfile
FROM --platform=linux/x86_64 python:3.8

WORKDIR /workspace
ADD . /workspace

RUN pip install -r requirements.txt

# Mandatory to run the jobs in rootless mode

RUN chown -R 42420:42420 /workspace

CMD [ "python3"  , "/workspace/train-audio-classification.py"]
```

Then, build the Docker image and push it in the registry:

```bash
docker build . -f Dockerfile -t <regristry-name>/marine-mammal-job:1.0.0
docker push <regristry-name>/marine-mammal-job:1.0.0
```

The output should be similar to this:

```bash
$ docker build . -f Dockerfile -t my-registry.c1.va1.container-registry.ovh.us/marine-mammal-job:1.0.0
...
 => => naming to my-registry.c1.va1.container-registry.ovh.us/marine-mammal-job:1.0.0

$ docker push my-registry.c1.va1.container-registry.ovh.us/marine-mammal-job:1.0.0                                                     
The push refers to repository [my-registry.c1.va1.container-registry.ovh.us/marine-mammal-job]
6e5b7acfda9e: Pushed 
..
1.0.0: digest: sha256:72f19493662aafe3d0a3dc35ea5ab76b8472bd6a709de2da1a52e7ebf8ab7ad1 size: 3054 
```

Once your Docker image is created and pushed into the registry, you can directly use the `ovhai` command to create your model training. You can launch the training specifying more or less GPU depending on the speed you want for your training.

**Note:**

If your images are stored in a private registry, please follow the documentation [Registries - Use & manage your registries](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/manage-registries.md) to add your registry.

```bash
ovhai job run \
    --name marine-audio-classification-job \
    --gpu 1 \
    --volume marine-mammal-model@US-EAST-VA/:/workspace/saved_model:RW:cache \
    --volume marine-mammal-sounds@US-EAST-VA/csv/:/workspace/data:RO:cache \
    <registry name>/marine-mammal-job:1.0.0
```

The output should be similar to this:

```bash
$ ovhai job run \
        --name marine-audio-classification-job \
        --gpu 1 \
        --volume marine-mammal-model@US-EAST-VA/:/workspace/saved_model:RW:cache \
        --volume marine-mammal-sounds@US-EAST-VA/csv/:/workspace/data:RO:cache \
        --unsecure-http \
        my-registry.c1.va1.container-registry.ovh.us/marine-mammal-job:1.0.0

Id:         c0c0878c-5564-4660-889a-65724f6e3056
Created At: 04-07-23 14:05:58
Updated At: 04-07-23 14:05:58
User:       my-user
Spec:
  Image:                my-registry.c1.va1.container-registry.ovh.us/marine-mammal-job:1.0.0
 Command:              
  Env Vars:             ~
  Default Http Port:    8080
  Unsecure Http:        true
  Resources:
    Gpu:               1
    Cpu:               13
    Memory:            40.0 GiB
    Public Network:    1.5 Gbps
    Private Network:   0 bps
    Ephemeral Storage: 750.0 GiB
    Gpu Model:         L4
    Gpu Brand:         NVIDIA
    Gpu Memory:        32.0 GiB
    Flavor:            l4-1-gpu
  Volumes:
  - Source:
      Container: marine-mammal-model
      Alias:     US-EAST-VA
      Prefix:    
      Archive:   ~
    Target: ~
    Mount:
      Mount Path: /workspace/saved_model
      Permission: Read & Write
    Cache:  true
  - Source:
      Container: marine-mammal-sounds
      Alias:     US-EAST-VA
      Prefix:    csv/
      Archive:   ~
    Target: ~
    Mount:
      Mount Path: /workspace/data
      Permission: Read Only
    Cache:  true
  Timeout:              0
  Timeout Auto Restart: false
  Shutdown:             ~
  Name:                 marine-audio-classification-job
  Labels:
    ovh/id:   c0c0878c-5564-4660-889a-65724f6e3056
    ovh/type: job
  Ssh Public Keys:      ~
Status:
  State:           QUEUED
  Ip:              ~
  External Ip:     ~
  Info:
    Message: Job submitted
  History:
    STATE      DATE
    QUEUED     20-07-23 16:08:58
  Data Sync:       ~
  Duration:        0s
  Url:             https://c0c0878c-5564-4660-889a-65724f6e3056.job.us-east-va.ai.cloud.ovh.us
  Info Url:        https://ui.us-east-va.ai.cloud.ovh.us/job/c0c0878c-5564-4660-889a-65724f6e3056
  Ssh Url:         ~
  Monitoring Url:  ~
  Volumes:
  - Mount Path:     /workspace/saved_model
    Id:             fbff59c9-abfa-4d7f-ae53-549348e8c53a
    User Volume Id: b51cd2f5-99a0-4cc0-bc32-ce4515ecce6f
  - Mount Path:     /workspace/data
    Id:             2f25d170-5741-459b-919b-fef8d6fc74c4
    User Volume Id: bf7b5c4b-c61e-4d17-b6d8-248de56834b6
```

You can access to the execution logs of your job with the CLI:

```bash
ovhai job logs <job id> -f       
```

The output should be similar to this:

```bash
$ ovhai job logs c0c0878c-5564-4660-889a-65724f6e3056 -f

Starting to watch job logs2023-07-04T15:13:08Z [job] 2023-07-04 15:13:08.579602: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-07-04T15:13:08Z [job] 2023-07-04 15:13:08.582765: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
...
2023-07-04T15:13:10Z [job] Skipping registering GPU devices...
2023-07-04T15:13:11Z [job] y:  [ 0  0  0 ... 44 44 44]
2023-07-04T15:13:11Z [job] X: [[-7.35979608e-01  2.06324223e-01  1.16956344e+00 ...  5.84987427e-01
2023-07-04T15:13:11Z [job]    5.93760708e-01 -4.47715498e-01]
2023-07-04T15:13:11Z [job]  [-6.79393058e-01  4.87389689e-01  1.43717311e+00 ...  6.24553399e-01
2023-07-04T15:13:11Z [job]    1.01619027e-01 -4.27615790e-01]
2023-07-04T15:13:11Z [job]  [-6.95846736e-01  1.96218503e-01  1.15618207e+00 ...  5.78436340e-01
2023-07-04T15:13:11Z [job]    9.53233744e-01 -1.29323842e-01]
2023-07-04T15:13:11Z [job]  ...
2023-07-04T15:13:11Z [job]  [-2.82393403e-01  6.98660564e-01 -8.70768342e-01 ...  4.16732718e-01
2023-07-04T15:13:11Z [job]    8.25026056e-01  1.64726948e-01]
2023-07-04T15:13:11Z [job]  [-1.06498353e-01 -1.16538834e-01  8.56199626e-01 ...  2.10513829e-01
2023-07-04T15:13:11Z [job]    1.61386821e-03  5.61172162e-01]
2023-07-04T15:13:11Z [job]  [ 1.77002149e+00 -6.27526483e-01  1.28201588e-02 ...  6.97330140e-01
2023-07-04T15:13:11Z [job]    5.26611477e-01  6.67499260e-01]]
2023-07-04T15:13:11Z [job] Model: "sequential"
2023-07-04T15:13:11Z [job] _________________________________________________________________
2023-07-04T15:13:11Z [job]  Layer (type)                Output Shape              Param #   
2023-07-04T15:13:11Z [job] =================================================================
2023-07-04T15:13:11Z [job]  dense (Dense)               (None, 512)               13824     
2023-07-04T15:13:11Z [job]                                                                  
2023-07-04T15:13:11Z [job]  dropout (Dropout)           (None, 512)               0         
2023-07-04T15:13:11Z [job]                                                                  
2023-07-04T15:13:11Z [job]  dense_1 (Dense)             (None, 256)               131328    
2023-07-04T15:13:11Z [job]                                                                  
2023-07-04T15:13:11Z [job]  dropout_1 (Dropout)         (None, 256)               0         
2023-07-04T15:13:11Z [job]                                                                  
2023-07-04T15:13:11Z [job]  dense_2 (Dense)             (None, 128)               32896     
2023-07-04T15:13:11Z [job]                                                                  
2023-07-04T15:13:11Z [job]  dropout_2 (Dropout)         (None, 128)               0         
2023-07-04T15:13:11Z [job]                                                                  
2023-07-04T15:13:11Z [job]  dense_3 (Dense)             (None, 64)                8256      
2023-07-04T15:13:11Z [job]                                                                  
2023-07-04T15:13:11Z [job]  dropout_3 (Dropout)         (None, 64)                0         
2023-07-04T15:13:11Z [job]                                                                  
2023-07-04T15:13:11Z [job]  dense_4 (Dense)             (None, 45)                2925      
2023-07-04T15:13:11Z [job]                                                                  
2023-07-04T15:13:11Z [job] =================================================================
2023-07-04T15:13:11Z [job] Total params: 189,229
2023-07-04T15:13:11Z [job] Trainable params: 189,229
2023-07-04T15:13:11Z [job] Non-trainable params: 0
2023-07-04T15:13:11Z [job] _________________________________________________________________
2023-07-04T15:13:11Z [job] None
2023-07-04T15:13:11Z [job] Epoch 1/100
82/82 [==============================] - 2s 13ms/step - loss: 0.1473 - accuracy: 0.9586 - val_loss: 0.1022 - val_accuracy: 0.9637
2023-07-04T15:13:13Z [job] Epoch 2/100
82/82 [==============================] - 1s 12ms/step - loss: 0.1220 - accuracy: 0.9597 - val_loss: 0.1044 - val_accuracy: 0.9668
2023-07-04T15:13:14Z [job] Epoch 3/100
...
```

For more explanations about the CLI command for AI Training, please read this guide: [CLI Reference](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/cli-overview.md).

Once you have your model ready, deploy the model to use it. This will be done with the AI Deploy tool.

 

## Go further

All the source code is available on the [OVHcloud GitHub organization](https://github.com/ovh/ai-training-examples/tree/main/jobs/audio/audio-classification).

Browse all the [AI & Machine Learning guides](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/landing-page-ai-machine-learning.md).

Browse the rest of the [OVHcloud US documentation](https://docs.us.ovhcloud.com/en/index.md) for guides on our other products and services.

Learn more about [AI & Machine Learning](https://us.ovhcloud.com/public-cloud/ai-machine-learning/) on the OVHcloud US website.

If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://us.ovhcloud.com/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.