2. Online Text-to-Speech (TTS)

1. Concept Introduction

1.1 What is "TTS"?

TTS technology is a technology that converts written text into human-audible speech output. It enables computers to "read" text content aloud, and is widely used in accessibility reading, intelligent assistants, navigation systems, educational software, and many other fields. Through TTS, users can hear naturally flowing human-like voices generated by machines, greatly enhancing the convenience and flexibility of information access.

1.2 Brief Description of Implementation Principles

The implementation of a TTS system mainly includes the following key steps and technologies:

1. Text Analysis

• In this stage, the input text is first preprocessed, including but not limited to removing irrelevant characters, standardizing punctuation and capitalization, word segmentation, and recognizing numbers and other special symbols and converting them into their corresponding word forms.
• At the same time, linguistic analysis of the text is also required, such as determining the pronunciation of each word (which usually requires a pronunciation dictionary), stress positions, intonation patterns, and sentence structure.

2. Language Processing

• This step mainly focuses on how to correctly pronounce and adjust intonation based on context. For example, the word "read" has different pronunciations in different tenses (pronounced as /rɛd/ in past tense/past participle, and /riːd/ in other cases). Therefore, a powerful language model is needed to understand these subtle differences.
• This also involves prosody modeling, which determines which parts should be emphasized, whether the speech rate should be fast or slow, and the emotional tone of the entire sentence.

3. Speech Synthesis

• The text information processed by the previous two stages is fed into a speech synthesis engine, which is responsible for generating the actual sound waveform.
• Traditional TTS systems use concatenative synthesis, which selects appropriate units from a pre-recorded speech segment database and concatenates them to form complete sentences. Although this method can produce high-quality speech, it is limited by the samples in the database.
• Modern TTS systems rely more on parametric synthesis or neural network synthesis (such as WaveNet, Tacotron, etc.), which can directly predict speech features from text and generate continuous sound signals. Especially deep learning-based methods, they can better capture the subtle variations of speech, thereby producing more natural and fluent voices.

4. Sound Waveform Generation

• Finally, the generated sound waveform is further processed to ensure its quality meets expected standards, such as adjusting volume, equalizing frequency response, etc.
• After that, the audio data can be played through speakers or other audio playback devices for people to listen to.

With the development of artificial intelligence and machine learning technology, especially the application of deep learning, TTS systems have not only improved significantly in accuracy but also made great progress in naturalness and emotional expression, making machine-generated speech increasingly close to real human voices.

2. Code Analysis

Key Code

1. TTS Initialization and Invocation (largemodel/largemodel/model_service.py)

​x
# From largemodel/largemodel/model_service.py
class LargeModelService(Node):
    def __init__(self):
        # ...
        self.system_sound_init()
        # ...
​
    def init_param_config(self):
        # ...
        self.declare_parameter('useolinetts', False)
        self.useolinetts = self.get_parameter('useolinetts').get_parameter_value().bool_value
        if self.useolinetts:
            self.tts_out_path = os.path.join(self.pkg_path, "resources_file", "tts_output.mp3")
        else:
            self.tts_out_path = os.path.join(self.pkg_path, "resources_file", "tts_output.wav")
​
    def system_sound_init(self):
        """Initialize TTS system"""
        model_type = "oline" if self.useolinetts else "local"
        self.model_client.tts_model_init(model_type, self.language)
        self.get_logger().info(f'TTS initialized with {model_type} model')
​
    def _safe_play_audio(self, text_to_speak: str):
        """
        Synthesizes and plays all non-empty messages only in non-text chat mode.
        """
        if not self.text_chat_mode and text_to_speak:
            try:
                self.model_client.voice_synthesis(text_to_speak, self.tts_out_path)
                self.play_audio_async(self.tts_out_path)
            except Exception as e:
                self.get_logger().error(f"Safe audio playback failed: {e}")

2. TTS Backend Implementation (largemodel/utils/large_model_interface.py)

xxxxxxxxxx
# From largemodel/utils/large_model_interface.py
class model_interface:
    # ...
    def tts_model_init(self,model_type='oline',language='zh'):
        if model_type=='oline':
            if self.tts_supplier=='baidu':
                self.token=self.fetch_token()
    
            self.model_type='oline'      
        elif model_type=='local':
            self.model_type='local'
            if language=='zh':
                tts_model=self.zh_tts_model
                tts_json=self.zh_tts_json
            elif language=='en':
                tts_model=self.en_tts_model
                tts_json=self.en_tts_json
            self.synthesizer = piper.PiperVoice.load(tts_model, config_path=tts_json, use_cuda=False)      
​
    def voice_synthesis(self,text,path):
        if self.model_type=='oline':
            if self.tts_supplier=='baidu':
                # ... (Baidu TTS implementation)
                pass
            elif self.tts_supplier=='aliyun':
                # ... (Aliyun TTS implementation)
                pass
        elif self.model_type=='local':
            with wave.open(path, 'wb') as wav_file:
                wav_file.setnchannels(1)
                wav_file.setsampwidth(2)
                wav_file.setframerate(self.synthesizer.config.sample_rate)
                self.synthesizer.synthesize(text, wav_file)

Code Analysis

The Text-to-Speech (TTS) function is initiated by the LargeModelService node, with the specific implementation provided by the model_interface class. Its design switches between different backend services through parameter configuration.

1. Initialization Flow (model_service.py):

   • During LargeModelService initialization, the init_param_config function reads the boolean value useolinetts from the ROS parameter server.
   • The system_sound_init function determines whether to pass the 'local' or 'oline' string to the self.model_client.tts_model_init method based on the value of useolinetts.
   • In large_model_interface.py, the tts_model_init method executes the corresponding initialization logic based on the received string parameter. If it is 'local', it uses piper.PiperVoice.load to load the local model file.

2. Synthesis and Playback Flow (model_service.py):

   • When voice broadcast is needed, the _safe_play_audio function is called.
   • This function first calls the self.model_client.voice_synthesis method, passing the text to be converted and the target audio path self.tts_out_path.
   • After the voice_synthesis method finishes executing and generates the audio file, _safe_play_audio then calls self.play_audio_async to asynchronously play the file.

3. Backend Implementation Selection (large_model_interface.py):

   • The voice_synthesis method serves as the backend distribution center for TTS functionality. It internally selects the execution path by checking the self.model_type attribute value set during initialization.
   • If self.model_type is 'local', the code block uses Python's wave library to open a WAV file, sets its header parameters (channels, sample bit width, sample rate), and then calls the self.synthesizer.synthesize method to write the audio stream synthesized from text directly into the file.
   • If self.model_type is 'oline', it enters the logic branch for different cloud service providers (such as Baidu, Aliyun).
   • This structure separates the upper-level node invocation ("say this passage") from the lower-level specific synthesis technology (which library to use, which API to call).

3. Practical Operations

3.1 Configuring Online TTS

1. First obtain an API Key from the Tongyi Qianwen or Baidu Qianfan registration tutorial

2. Open the model interface configuration file large_model_interface.yaml:

   xxxxxxxxxx
   vim ~/yahboom_ws/install/largemodel/share/largemodel/config/large_model_interface.yaml
   

3. Enter your API credentials: Find the online TTS section. Here we use Tongyi Qianwen as an example. Enter your Key, and you can also specify a voice tone name you prefer.

   xxxxxxxxxx
   # Large Language Models Settings
   # -----------------------------------------------------------------------------
   ## Online Large Language Models
   # Tongyi Platform Configuration
   tongyi_api_key: "Your_AccessKey_ID"  # API Key
   ​
   ​
   # -----------------------------------------------------------------------------
   # Text-to-Speech Settings
   # -----------------------------------------------------------------------------
   ## General TTS Settings
   tts_supplier: "aliyun"  # TTS provider, options: "aliyun" or "baidu"
   ​
   ## Online TTS
   # Alibaba Cloud Tongyi Qianwen Speech Synthesis Configuration
   oline_tts_model: "cosyvoice-v2"  # Speech model
   voice_tone: "longwan_v2"         # Voice tone
   

4. Open the main configuration file yahboom.yaml:

   xxxxxxxxxx
   vim ~/yahboom_ws/install/largemodel/share/largemodel/config/yahboom.yaml
   

5. Enable online TTS mode: Set the useolinetts parameter to True.

   xxxxxxxxxx
   # yahboom.yaml
   ​
   model_service:
     ros__parameters:
       useolinetts: True  # Critical: Change from False to True
   

3.2 Startup and Testing

1. Start the TTS node: Run the following command:

   xxxxxxxxxx
   ros2 launch largemodel tts_only.launch.py
   

   

2. Send text to be synthesized: Open a new terminal and run the following command to publish a voice broadcast message:

   xxxxxxxxxx
   ros2 topic pub --once /tts_text_input std_msgs/msg/String '{data: "Speech synthesis test successful"}'
   

3. Test: If everything is working properly, you should hear the robot speak "Speech synthesis test successful" in synthesized voice from your speaker.