中文说明

安装

GPU Support with CUDA (recommended)

Additional steps are needed for a GPU-optimized installation. These steps are recommended for those who require better performance and have a compatible NVIDIA GPU.

Note: To check if your NVIDIA GPU supports CUDA, visit the official CUDA GPUs list.

To use RealtimeSTT with GPU support via CUDA please follow these steps:

1. Install NVIDIA CUDA Toolkit 11.8:

   • Visit NVIDIA CUDA Toolkit Archive.
   • Select operating system and version.
   • Download and install the software.

2. Install NVIDIA cuDNN 8.7.0 for CUDA 11.x:

   • Visit NVIDIA cuDNN Archive.
   • Click on "Download cuDNN v8.7.0 (November 28th, 2022), for CUDA 11.x".
   • Download and install the software.

3. Install ffmpeg:

   You can download an installer for your OS from the ffmpeg Website.
   Or use a package manager:

   • On Ubuntu or Debian:

     sudo apt update && sudo apt install ffmpeg

   • On Arch Linux:

     sudo pacman -S ffmpeg

   • On MacOS using Homebrew (https://brew.sh/):

     brew install ffmpeg

   • On Windows using Chocolatey (https://chocolatey.org/):

     choco install ffmpeg

   • On Windows using Scoop (https://scoop.sh/):

     scoop install ffmpeg

4. Install PyTorch with CUDA support:

       pip uninstall torch
       pip install torch==2.0.1+cu118 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118
   

其他依赖

pip install -r requirements.txt

Win如果装不上webrtcvad，提示了VS C++相关的报错，请下载https://visualstudio.microsoft.com/zh-hans/visual-cpp-build-tools/，安装C++开发相关工具，然后重新安装依赖。

使用说明

启动后端 python RealtimeSTT_server2.py

双击index.html，浏览器运行，填入服务器IP地址，注意，关闭服务器的防火墙，或者针对性放行9001/9002 这两个用于websocket连接的端口！

等待后端模型加载完毕后，就可以正常对话了。

更新日志

• 2024-07-01

  • 修复webui，聊天类型无法保存的bug
  • 给webui的OpenAI的模型增加自定义配置功能，可以删除后，自定义完回车保存配置

• 2024-06-03

  • 增加了 唤醒词的配置，未启用唤醒词功能，测试了下可以通过唤醒词触发录音。唤醒一次，说一些话。

• 2024-06-02

  • 新增了OpenAI接口的接入，测了ollama，没啥问题
  • 新增了Edge-TTS的接入（方便测试）

• 2024-05-28

  • 补充个webui，方便配置（不过并不完整，凑合用）
  • 补充了gpt-sovits的新api的兼容

RealtimeSTT

Easy-to-use, low-latency speech-to-text library for realtime applications

About the Project

RealtimeSTT listens to the microphone and transcribes voice into text.

It's ideal for:

• Voice Assistants
• Applications requiring fast and precise speech-to-text conversion

RealtimeSTT.mp4

Updates

v0.1.9

• switched to torch.multiprocessing
• added compute_type, input_device_index and gpu_device_index parameters
• recorder.text() interruptable with recorder.abort()
• fix for #20

v0.1.8

• added example how to realtime transcribe from browser microphone
• large-v3 whisper model now supported (upgrade to faster_whisper 0.10.0)
• added feed_audio() and use_microphone parameter to feed chunks

v0.1.7

• Bugfix for Mac OS Installation (multiprocessing / queue.size())
• KeyboardInterrupt handling (now abortable with CTRL+C)
• Bugfix for spinner handling (could lead to exception in some cases)

v0.1.6

• Implements context manager protocol (recorder can be used in a with statement)
• Bugfix for resource management in shutdown method

v0.1.5

• Bugfix for detection of short speech right after sentence detection (the problem mentioned in the video)
• Main transcription and recording moved into separate process contexts with multiprocessing

Hint: Since we use the multiprocessing module now, ensure to include the if __name__ == '__main__': protection in your code to prevent unexpected behavior, especially on platforms like Windows. For a detailed explanation on why this is important, visit the official Python documentation on multiprocessing.

Features

• Voice Activity Detection: Automatically detects when you start and stop speaking.
• Realtime Transcription: Transforms speech to text in real-time.
• Wake Word Activation: Can activate upon detecting a designated wake word.

Hint: Check out RealtimeTTS, the output counterpart of this library, for text-to-voice capabilities. Together, they form a powerful realtime audio wrapper around large language models.

Tech Stack

This library uses:

• Voice Activity Detection
  • WebRTCVAD for initial voice activity detection.
  • SileroVAD for more accurate verification.
• Speech-To-Text
  • Faster_Whisper for instant (GPU-accelerated) transcription.
• Wake Word Detection
  • Porcupine for wake word detection.

These components represent the "industry standard" for cutting-edge applications, providing the most modern and effective foundation for building high-end solutions.

Installation 安装

pip install RealtimeSTT

This will install all the necessary dependencies, including a CPU support only version of PyTorch.

Although it is possible to run RealtimeSTT with a CPU installation only (use a small model like "tiny" or "base" in this case) you will get way better experience using:

GPU Support with CUDA (recommended)

Additional steps are needed for a GPU-optimized installation. These steps are recommended for those who require better performance and have a compatible NVIDIA GPU.

Note: To check if your NVIDIA GPU supports CUDA, visit the official CUDA GPUs list.

To use RealtimeSTT with GPU support via CUDA please follow these steps:

1. Install NVIDIA CUDA Toolkit 11.8:

   • Visit NVIDIA CUDA Toolkit Archive.
   • Select operating system and version.
   • Download and install the software.

2. Install NVIDIA cuDNN 8.7.0 for CUDA 11.x:

   • Visit NVIDIA cuDNN Archive.
   • Click on "Download cuDNN v8.7.0 (November 28th, 2022), for CUDA 11.x".
   • Download and install the software.

3. Install ffmpeg:

   You can download an installer for your OS from the ffmpeg Website.

   Or use a package manager:

   • On Ubuntu or Debian:

     sudo apt update && sudo apt install ffmpeg

   • On Arch Linux:

     sudo pacman -S ffmpeg

   • On MacOS using Homebrew (https://brew.sh/):

     brew install ffmpeg

   • On Windows using Chocolatey (https://chocolatey.org/):

     choco install ffmpeg

   • On Windows using Scoop (https://scoop.sh/):

     scoop install ffmpeg

4. Install PyTorch with CUDA support:

   pip uninstall torch
   pip install torch==2.0.1+cu118 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118

Quick Start

Basic usage:

Manual Recording

Start and stop of recording are manually triggered.

recorder.start()
recorder.stop()
print(recorder.text())

Automatic Recording

Recording based on voice activity detection.

with AudioToTextRecorder() as recorder:
    print(recorder.text())

When running recorder.text in a loop it is recommended to use a callback, allowing the transcription to be run asynchronously:

def process_text(text):
    print (text)
    
while True:
    recorder.text(process_text)

Wakewords

Keyword activation before detecting voice. Write the comma-separated list of your desired activation keywords into the wake_words parameter. You can choose wake words from these list: alexa, americano, blueberry, bumblebee, computer, grapefruits, grasshopper, hey google, hey siri, jarvis, ok google, picovoice, porcupine, terminator.

recorder = AudioToTextRecorder(wake_words="jarvis")

print('Say "Jarvis" then speak.')
print(recorder.text())

Callbacks

You can set callback functions to be executed on different events (see Configuration) :

def my_start_callback():
    print("Recording started!")

def my_stop_callback():
    print("Recording stopped!")

recorder = AudioToTextRecorder(on_recording_start=my_start_callback,
                               on_recording_stop=my_stop_callback)

Feed chunks

If you don't want to use the local microphone set use_microphone parameter to false and provide raw PCM audiochunks in 16-bit mono (samplerate 16000) with this method:

recorder.feed_audio(audio_chunk)

Shutdown

You can shutdown the recorder safely by using the context manager protocol:

with AudioToTextRecorder() as recorder:
    [...]

Or you can call the shutdown method manually (if using "with" is not feasible):

recorder.shutdown()

Testing the Library

The test subdirectory contains a set of scripts to help you evaluate and understand the capabilities of the RealtimeTTS library.

Test scripts depending on RealtimeTTS library may require you to enter your azure service region within the script. When using OpenAI-, Azure- or Elevenlabs-related demo scripts the API Keys should be provided in the environment variables OPENAI_API_KEY, AZURE_SPEECH_KEY and ELEVENLABS_API_KEY (see RealtimeTTS)

• simple_test.py

  • Description: A "hello world" styled demonstration of the library's simplest usage.

• realtimestt_test.py

  • Description: Showcasing live-transcription.

• wakeword_test.py

  • Description: A demonstration of the wakeword activation.

• translator.py

  • Dependencies: Run pip install openai realtimetts.
  • Description: Real-time translations into six different languages.

• openai_voice_interface.py

  • Dependencies: Run pip install openai realtimetts.
  • Description: Wake word activated and voice based user interface to the OpenAI API.

• advanced_talk.py

  • Dependencies: Run pip install openai keyboard realtimetts.
  • Description: Choose TTS engine and voice before starting AI conversation.

• minimalistic_talkbot.py

  • Dependencies: Run pip install openai realtimetts.
  • Description: A basic talkbot in 20 lines of code.

The example_app subdirectory contains a polished user interface application for the OpenAI API based on PyQt5.

Configuration

Initialization Parameters for AudioToTextRecorder

When you initialize the AudioToTextRecorder class, you have various options to customize its behavior.

General Parameters

• model (str, default="tiny"): Model size or path for transcription.

  • Options: 'tiny', 'tiny.en', 'base', 'base.en', 'small', 'small.en', 'medium', 'medium.en', 'large-v1', 'large-v2'.
  • Note: If a size is provided, the model will be downloaded from the Hugging Face Hub.

• language (str, default=""): Language code for transcription. If left empty, the model will try to auto-detect the language. Supported language codes are listed in Whisper Tokenizer library.

• compute_type (str, default="default"): Specifies the type of computation to be used for transcription. See Whisper Quantization

• input_device_index (int, default=0): Audio Input Device Index to use.

• gpu_device_index (int, default=0): GPU Device Index to use. The model can also be loaded on multiple GPUs by passing a list of IDs (e.g. [0, 1, 2, 3]).

• on_recording_start: A callable function triggered when recording starts.

• on_recording_stop: A callable function triggered when recording ends.

• on_transcription_start: A callable function triggered when transcription starts.

• ensure_sentence_starting_uppercase (bool, default=True): Ensures that every sentence detected by the algorithm starts with an uppercase letter.

• ensure_sentence_ends_with_period (bool, default=True): Ensures that every sentence that doesn't end with punctuation such as "?", "!" ends with a period

• use_microphone (bool, default=True): Usage of local microphone for transcription. Set to False if you want to provide chunks with feed_audio method.

• spinner (bool, default=True): Provides a spinner animation text with information about the current recorder state.

• level (int, default=logging.WARNING): Logging level.

Real-time Transcription Parameters

Note: When enabling realtime description a GPU installation is strongly advised. Using realtime transcription may create high GPU loads.

• enable_realtime_transcription (bool, default=False): Enables or disables real-time transcription of audio. When set to True, the audio will be transcribed continuously as it is being recorded.

• realtime_model_type (str, default="tiny"): Specifies the size or path of the machine learning model to be used for real-time transcription.

  • Valid options: 'tiny', 'tiny.en', 'base', 'base.en', 'small', 'small.en', 'medium', 'medium.en', 'large-v1', 'large-v2'.

• realtime_processing_pause (float, default=0.2): Specifies the time interval in seconds after a chunk of audio gets transcribed. Lower values will result in more "real-time" (frequent) transcription updates but may increase computational load.

• on_realtime_transcription_update: A callback function that is triggered whenever there's an update in the real-time transcription. The function is called with the newly transcribed text as its argument.

• on_realtime_transcription_stabilized: A callback function that is triggered whenever there's an update in the real-time transcription and returns a higher quality, stabilized text as its argument.

Voice Activation Parameters

• silero_sensitivity (float, default=0.6): Sensitivity for Silero's voice activity detection ranging from 0 (least sensitive) to 1 (most sensitive). Default is 0.6.

• silero_sensitivity (float, default=0.6): Sensitivity for Silero's voice activity detection ranging from 0 (least sensitive) to 1 (most sensitive). Default is 0.6.

• silero_use_onnx (bool, default=False): Enables usage of the pre-trained model from Silero in the ONNX (Open Neural Network Exchange) format instead of the PyTorch format. Default is False. Recommended for faster performance.

• post_speech_silence_duration (float, default=0.2): Duration in seconds of silence that must follow speech before the recording is considered to be completed. This ensures that any brief pauses during speech don't prematurely end the recording.

• min_gap_between_recordings (float, default=1.0): Specifies the minimum time interval in seconds that should exist between the end of one recording session and the beginning of another to prevent rapid consecutive recordings.

• min_length_of_recording (float, default=1.0): Specifies the minimum duration in seconds that a recording session should last to ensure meaningful audio capture, preventing excessively short or fragmented recordings.

• pre_recording_buffer_duration (float, default=0.2): The time span, in seconds, during which audio is buffered prior to formal recording. This helps counterbalancing the latency inherent in speech activity detection, ensuring no initial audio is missed.

• on_vad_detect_start: A callable function triggered when the system starts to listen for voice activity.

• on_vad_detect_stop: A callable function triggered when the system stops to listen for voice activity.

Wake Word Parameters

• wake_words (str, default=""): Wake words for initiating the recording. Multiple wake words can be provided as a comma-separated string. Supported wake words are: alexa, americano, blueberry, bumblebee, computer, grapefruits, grasshopper, hey google, hey siri, jarvis, ok google, picovoice, porcupine, terminator

• wake_words_sensitivity (float, default=0.6): Sensitivity level for wake word detection (0 for least sensitive, 1 for most sensitive).

• wake_word_activation_delay (float, default=0): Duration in seconds after the start of monitoring before the system switches to wake word activation if no voice is initially detected. If set to zero, the system uses wake word activation immediately.

• wake_word_timeout (float, default=5): Duration in seconds after a wake word is recognized. If no subsequent voice activity is detected within this window, the system transitions back to an inactive state, awaiting the next wake word or voice activation.

• on_wakeword_detected: A callable function triggered when a wake word is detected.

• on_wakeword_timeout: A callable function triggered when the system goes back to an inactive state after when no speech was detected after wake word activation.

• on_wakeword_detection_start: A callable function triggered when the system starts to listen for wake words

• on_wakeword_detection_end: A callable function triggered when stopping to listen for wake words (e.g. because of timeout or wake word detected)

Contribution

Contributions are always welcome!

License

MIT

Author

Kolja Beigel
Email: [email protected]
GitHub