How to use the command "tts" (with examples)

The tts command is a tool that allows you to synthesize speech using different models. It provides a way to convert text into spoken words, offering a wide range of models to choose from. This command is particularly useful for tasks such as voiceover generation, virtual assistants, and audio content creation.

Use case 1: Run text-to-speech with the default models, writing the output to “tts_output.wav”

Code:

tts --text "text"

Motivation: This use case is helpful when you want to quickly convert a text into speech using the default models. It’s a straightforward command that allows you to generate speech output without any additional configurations.

Explanation:

• --text "text": This argument specifies the input text to be converted into speech.

Example output: The output of this command will be a file named “tts_output.wav” containing the synthesized speech based on the provided text.

Use case 2: List provided models

Code:

tts --list_models

Motivation: Sometimes, you may want to explore the available models to select the most suitable one for your specific needs. This command provides a convenient way to list all the provided models supported by the tts command.

Explanation: This command does not require any additional arguments. It simply retrieves a list of all the available models that can be used for text-to-speech synthesis.

Example output: The command will output a list of models supported by the tts command, such as “model_type/language/dataset/model_name”.

Use case 3: Query info for a model by idx

Code:

tts --model_info_by_idx model_type/model_query_idx

Motivation: If you have a particular model in mind and know its index (idx), this command allows you to obtain detailed information about that specific model. You can find out specific details such as the model’s type, language, dataset, and name.

Explanation:

• --model_info_by_idx model_type/model_query_idx: This argument allows you to query model information based on its index (idx). The model_type specifies the type of the model, such as “fastpitch” or “tacotron2”, and model_query_idx represents the index number associated with the model.

Example output: Running this command with the appropriate arguments will provide detailed information about the specified model, including its type, language, dataset, and name.

Use case 4: Query info for a model by name

Code:

tts --model_info_by_name model_type/language/dataset/model_name

Motivation: If you already know the name of a model and want to gather more information about it, this command allows you to specifically query model details based on its name.

Explanation:

• --model_info_by_name model_type/language/dataset/model_name: This argument enables you to query model information using its name. The model_type specifies the type of the model, language represents the language, dataset is the dataset name, and model_name represents the specific name of the model.

Example output: By running this command with the correct model name, you can obtain detailed information about the specific model, including its type, language, dataset, and other relevant details.

Use case 5: Run a text-to-speech model with its default vocoder model

Code:

tts --text "text" --model_name model_type/language/dataset/model_name

Motivation: When you have a specific model in mind and want to synthesize speech using that model along with its default vocoder model, this command is useful. It allows you to specify the input text and the desired model.

Explanation:

• --text "text": This argument specifies the input text to be converted into speech.
• --model_name model_type/language/dataset/model_name: This argument selects the desired model for text-to-speech synthesis. The model_type specifies the type of the model, language represents the language, dataset is the dataset name, and model_name refers to the specific name of the model.

Example output: Running this command will generate synthesized speech using the specified model and save it to an output file.

Use case 6: Run your own text-to-speech model (using the Griffin-Lim vocoder)

Code:

tts --text "text" --model_path path/to/model.pth --config_path path/to/config.json --out_path path/to/file.wav

Motivation: If you have trained your own text-to-speech model or want to use a custom model, this command allows you to utilize your own model for synthesizing speech. It gives you the flexibility to specify the model’s path, the configuration file, and the desired output file path.

Explanation:

• --text "text": This argument specifies the input text to be converted into speech.
• --model_path path/to/model.pth: This argument represents the path to your own text-to-speech model.
• --config_path path/to/config.json: This argument specifies the path to the configuration file associated with your custom model.
• --out_path path/to/file.wav: This argument determines the desired output file path and format.

Example output: By running this command with the appropriate paths and configurations, you can generate synthesized speech using your own custom text-to-speech model and save the output to the specified file path.

Conclusion:

The tts command provides a range of functionalities for text-to-speech synthesis. Whether you need to convert text into speech using default models or utilize your own custom models, this command offers flexibility and control over the speech synthesis process. With the ability to list models, query model information, and choose from different vocoder options, the tts command is a valuable tool for various applications, such as voiceovers, virtual assistants, and multimedia content creation.