<p align="center">
<img src="dspeech.png" alt="DCheck Logo" width="250">
</p>
# DSpeech: A Command-line Speech Processing Toolkit
[中文](README_zh.md) | English
DSpeech is an advanced command-line toolkit designed for speech processing tasks such as transcription, voice activity detection (VAD), punctuation addition, and emotion classification. It is built on top of state-of-the-art models and provides an easy-to-use interface for handling various speech processing jobs.
## 1. Installation
### 1.1 Prerequisites
- Python 3.6 or later
- PyTorch 1.7 or later
- torchaudio
- rich
- soundfile
- funasr (A lightweight AutoModel library for speech processing)
### 1.2 Installation Steps
1. Clone the repository:
```bash
git clone https://gitee.com/iint/dspeech.git
cd dspeech
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Directly install dspeech via pip:
```bash
pip install dspeech
```
4. Set the `DSPEECH_HOME` environment variable to the directory where your models are stored:
```bash
export DSPEECH_HOME=/path/to/dspeech/models
```
5. Download the necessary models and place them in the `DSPEECH_HOME` directory. You can download the models using the following commands (replace `<model_id>` with the actual model ID):
```bash
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download --resume-download <model_id> --local-dir $DSPEECH_HOME/<model_name>
```
6. (Optional) Also you can install Dguard if you want to do the speaker diarization task:
```bash
pip install dguard==0.1.20
export DGUARD_MODEL_PATH=<path to dguard model home>
dguard_info
```
7. Print the help message to see the available commands:
```bash
dspeech help
```
You should see a list of available commands and options.
```
DSpeech: A Command-line Speech Processing Toolkit
Usage: dspeech
Commands:
help Show this help message
transcribe Transcribe an audio file
vad Perform VAD on an audio file
punc Add punctuation to a text
emo Perform emotion classification on an audio file
clone Clone speaker's voice and generate audio
clone_with_emo Clone speaker's voice with emotion and generate audio
Options (for asr and emotion classify):
--model Model name (default: sensevoicesmall)
--vad-model VAD model name (default: fsmn-vad)
--punc-model Punctuation model name (default: ct-punc)
--emo-model Emotion model name (default: emotion2vec_plus_large)
--device Device to run the models on (default: cuda)
--file Audio file path for transcribing, VAD, or emotion classification
--text Text to process with punctuation model
--start Start time in seconds for processing audio files (default: 0)
--end End time in seconds for processing audio files (default: end of file)
--sample-rate Sample rate of the audio file (default: 16000)
Options (for tts):
--ref_audio Reference audio file path for voice cloning
--ref_text Reference text for voice cloning
--speaker_folder Speaker folder path for emotional voice cloning
--text Text to generate audio
--audio_save_path Path to save the audio
--spectrogram_save_path * [Optional] Path to save the spectrogram
--speed Speed of the audio
--sample_rate Sample rate of the audio file (default: 16000)
Example: dspeech transcribe --file audio.wav
```
## 2. Features
DSpeech offers the following functionalities:
- **Transcription**: Convert audio files to text using state-of-the-art speech recognition models.
- **Voice Activity Detection (VAD)**: Detect and segment speech regions in an audio file.
- **Punctuation Addition**: Add punctuation to raw text transcriptions to improve readability.
- **Emotion Classification**: Classify the emotional content of an audio file into various categories.
- **Voice Cloning**: Clone a voice from a given audio file using a text-to-speech (TTS) model.
- **Emotion TTS**: Generate emotional speech using a text-to-speech (TTS) model.
## 3. Introduction to the dspeech.STT
To use DSpeech in a Python script, you can import the `STT` class and create an instance with the desired models:
```python
from dspeech.stt import STT
# Initialize the STT handler with the specified models
handler = STT(model_name="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc", emo_model="emotion2vec_plus_large")
# Transcribe an audio file
transcription = handler.transcribe_file("audio.wav")
print(transcription)
# Perform VAD on an audio file
vad_result = handler.vad_file("audio.wav")
print(vad_result)
# Add punctuation to a text
punctuation_text = handler.punc_result("this is a test")
print(punctuation_text)
# Perform emotion classification on an audio file
emotion_result = handler.emo_classify_file("audio.wav")
print(emotion_result)
```
## 4. Introduction to the dspeech.TTS
### 4.1 Initialization
To initialize the `TTS` module, create a `TTS` handler object specifying the target device (CPU or GPU) and sample rate for generated audio.
```python
from dspeech import TTS
import torch
# Initialize TTS handler
tts_handler = TTS(
device="cuda", # Use "cpu" if no GPU is available
target_sample_rate=24000 # Define target sample rate for output audio
)
```
The `device` parameter can be set to "cuda" for GPU usage or "cpu" for running on the CPU.
### 4.2 Basic Voice Cloning
In basic voice cloning, you provide a reference audio and text, and the system generates a new speech that mimics the voice from the reference audio with the content of the provided text.
```python
import torchaudio
# Load reference audio using torchaudio
ref_audio, sample_rate = torchaudio.load("tests/a.wav")
# Clone voice based on reference audio and text
r = tts_handler.clone(
ref_audio=(ref_audio, sample_rate), # Reference audio in (Tensor, int) format or file path
ref_text="Reference text", # The transcription of the reference audio
gen_text_batches=["Hello, my name is Xiao Ming", "I am an AI", "I can speak Chinese"], # Text to generate speech for
speed=1, # Speech speed (1 is normal speed)
channel=-1, # Merge all channels (-1) or specify one channel
remove_silence=True, # Option to remove silence from the reference audio
wave_path="tests/tts_output.wav", # Path to save generated audio
spectrogram_path="tests/tts_output.png", # Path to save spectrogram of generated audio
concat=True # Whether to merge all generated audio into a single output file
)
```
**Parameters:**
- `ref_audio`: The reference audio in the format `(Tensor, int)` or as a file path.
- `ref_text`: The transcription of the reference audio.
- `gen_text_batches`: A list of text strings that you want to convert into speech.
- `speed`: Adjusts the speed of the generated speech (default is 1, for normal speed).
- `remove_silence`: Option to remove silence from the reference audio (boolean).
- `wave_path`: Path to save the generated audio file.
- `spectrogram_path`: Path to save the spectrogram image of the generated audio.
### 4.3 Extracting Speaker Information**
For complex voice cloning with multiple speakers and emotions, you need to extract speaker information from a directory containing multiple audio files for different speakers and emotions.
The directory should have the following structure:
```
<path>/
├── speaker1/
│ ├── happy.wav
│ ├── happy.txt
│ ├── neutral.wav
│ ├── neutral.txt
```
Each subdirectory represents a speaker, and each audio file should have an accompanying text file.
```python
# Extract speaker information from the folder
spk_info = tts_handler.get_speaker_info("tests/speaker")
print(spk_info)
```
This function returns a dictionary of speaker information, which will be used for advanced cloning tasks.
### 4.4 Voice Cloning with Emotions**
To clone a voice with emotional expressions, you can use the `clone_with_emo` method. The generated text should contain emotional markers, e.g., `[[zhaosheng_angry]]`, where `zhaosheng` is the speaker and `angry` is the emotion.
```python
r = tts_handler.clone_with_emo(
gen_text_batches=[
"[[zhaosheng_angry]] How could you talk to me like that? It's too much!",
"[[zhaosheng_whisper]] Be careful, don't let anyone hear, it's a secret.",
"[[zhaosheng_sad]] I'm really sad, things are out of my control."
],
speaker_info=spk_info, # Dictionary of speaker information
speed=1, # Speech speed
channel=-1, # Merge all channels
remove_silence=True, # Remove silence in the generated output
wave_path="tests/tts_output_emo.wav", # Path to save output audio with emotions
spectrogram_path="tests/tts_output_emo.png" # Path to save spectrogram with emotions
)
```
### 4.5 Multi-Speaker and Multi-Emotion Dialogues
For generating dialogues between multiple speakers with different emotions, make sure the directory `tests/speaker` contains subdirectories for each speaker, and the corresponding audio and text files exist for each emotion.
```python
# Extract speaker information for multiple speakers
spk_info = tts_handler.get_speaker_info("tests/speaker")
# Generate multi-speaker and multi-emotion dialogue
r = tts_handler.clone_with_emo(
gen_text_batches=[
"[[zhaosheng_angry]] How could you talk to me like that? It's too much!",
"[[duanyibo_whisper]] Be careful, don't let anyone hear, it's a secret.",
"[[zhaosheng_sad]] I'm really sad, things are out of my control."
],
speaker_info=spk_info, # Speaker information extracted from directory
speed=1, # Speech speed
channel=-1, # Merge all channels
remove_silence=True, # Remove silence from the reference audio
wave_path="tests/tts_output_emo.wav", # Path to save generated audio
spectrogram_path="tests/tts_output_emo.png" # Path to save generated spectrogram
)
```
This method will generate a single audio file containing speech from multiple speakers with different emotional expressions.
### 4.6 Output Files**
- **Wave Path (`wave_path`)**: Specifies where to save the generated audio output. If `concat=True`, all `gen_text_batches` will be concatenated into one audio file.
- **Spectrogram Path (`spectrogram_path`)**: Specifies where to save the spectrogram image of the generated speech. This is useful for visual analysis of the audio.
## Command-line Interface
DSpeech provides a command-line interface for quick and easy access to its functionalities. To see the available commands, run:
```bash
dspeech help
DSpeech: A Command-line Speech Processing Toolkit
Usage: dspeech
Commands:
transcribe Transcribe an audio file
vad Perform VAD on an audio file
punc Add punctuation to a text
emo Perform emotion classification on an audio file
Options:
--model Model name (default: sensevoicesmall)
--vad-model VAD model name (default: fsmn-vad)
--punc-model Punctuation model name (default: ct-punc)
--emo-model Emotion model name (default: emotion2vec_plus_large)
--device Device to run the models on (default: cuda)
--file Audio file path for transcribing, VAD, or emotion classification
--text Text to process with punctuation model
--start Start time in seconds for processing audio files (default: 0)
--end End time in seconds for processing audio files (default: end of file)
--sample-rate Sample rate of the audio file (default: 16000)
Example: dspeech transcribe --file audio.wav
```
### Usage Examples
- **Transcribe an audio file**:
```bash
dspeech transcribe --file audio.wav
```
- **Perform VAD on an audio file**:
```bash
dspeech vad --file audio.wav
```
- **Add punctuation to a text**:
```bash
dspeech punc --text "this is a test"
```
- **Perform emotion classification on an audio file**:
```bash
dspeech emo --file audio.wav
```
## License
DSpeech is licensed under the MIT License. See the LICENSE file for more details.
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"description": "<p align=\"center\">\n <img src=\"dspeech.png\" alt=\"DCheck Logo\" width=\"250\">\n</p>\n\n# DSpeech: A Command-line Speech Processing Toolkit\n[\u4e2d\u6587](README_zh.md) | English\n\n\nDSpeech is an advanced command-line toolkit designed for speech processing tasks such as transcription, voice activity detection (VAD), punctuation addition, and emotion classification. It is built on top of state-of-the-art models and provides an easy-to-use interface for handling various speech processing jobs.\n\n## 1. Installation\n\n### 1.1 Prerequisites\n- Python 3.6 or later\n- PyTorch 1.7 or later\n- torchaudio\n- rich\n- soundfile\n- funasr (A lightweight AutoModel library for speech processing)\n\n### 1.2 Installation Steps\n1. Clone the repository:\n ```bash\n git clone https://gitee.com/iint/dspeech.git\n cd dspeech\n ```\n2. Install the required packages:\n ```bash\n pip install -r requirements.txt\n ```\n\n3. Directly install dspeech via pip:\n ```bash\n pip install dspeech\n ```\n4. Set the `DSPEECH_HOME` environment variable to the directory where your models are stored:\n ```bash\n export DSPEECH_HOME=/path/to/dspeech/models\n ```\n5. Download the necessary models and place them in the `DSPEECH_HOME` directory. You can download the models using the following commands (replace `<model_id>` with the actual model ID):\n ```bash\n export HF_ENDPOINT=https://hf-mirror.com\n huggingface-cli download --resume-download <model_id> --local-dir $DSPEECH_HOME/<model_name>\n ```\n\n6. (Optional) Also you can install Dguard if you want to do the speaker diarization task:\n ```bash\n pip install dguard==0.1.20\n export DGUARD_MODEL_PATH=<path to dguard model home>\n dguard_info\n ```\n\n7. Print the help message to see the available commands:\n ```bash\n dspeech help\n ```\n You should see a list of available commands and options.\n ```\n DSpeech: A Command-line Speech Processing Toolkit\n Usage: dspeech \n Commands:\n help Show this help message\n transcribe Transcribe an audio file\n vad Perform VAD on an audio file\n punc Add punctuation to a text\n emo Perform emotion classification on an audio file\n clone Clone speaker's voice and generate audio\n clone_with_emo Clone speaker's voice with emotion and generate audio\n Options (for asr and emotion classify):\n --model Model name (default: sensevoicesmall)\n --vad-model VAD model name (default: fsmn-vad)\n --punc-model Punctuation model name (default: ct-punc)\n --emo-model Emotion model name (default: emotion2vec_plus_large)\n --device Device to run the models on (default: cuda)\n --file Audio file path for transcribing, VAD, or emotion classification\n --text Text to process with punctuation model\n --start Start time in seconds for processing audio files (default: 0)\n --end End time in seconds for processing audio files (default: end of file)\n --sample-rate Sample rate of the audio file (default: 16000)\n Options (for tts):\n --ref_audio Reference audio file path for voice cloning\n --ref_text Reference text for voice cloning\n --speaker_folder Speaker folder path for emotional voice cloning\n --text Text to generate audio\n --audio_save_path Path to save the audio\n --spectrogram_save_path * [Optional] Path to save the spectrogram\n --speed Speed of the audio\n --sample_rate Sample rate of the audio file (default: 16000)\n Example: dspeech transcribe --file audio.wav\n ```\n\n## 2. Features\nDSpeech offers the following functionalities:\n- **Transcription**: Convert audio files to text using state-of-the-art speech recognition models.\n- **Voice Activity Detection (VAD)**: Detect and segment speech regions in an audio file.\n- **Punctuation Addition**: Add punctuation to raw text transcriptions to improve readability.\n- **Emotion Classification**: Classify the emotional content of an audio file into various categories.\n- **Voice Cloning**: Clone a voice from a given audio file using a text-to-speech (TTS) model.\n- **Emotion TTS**: Generate emotional speech using a text-to-speech (TTS) model.\n\n## 3. Introduction to the dspeech.STT\nTo use DSpeech in a Python script, you can import the `STT` class and create an instance with the desired models:\n\n```python\nfrom dspeech.stt import STT\n# Initialize the STT handler with the specified models\nhandler = STT(model_name=\"paraformer-zh\", vad_model=\"fsmn-vad\", punc_model=\"ct-punc\", emo_model=\"emotion2vec_plus_large\")\n# Transcribe an audio file\ntranscription = handler.transcribe_file(\"audio.wav\")\nprint(transcription)\n# Perform VAD on an audio file\nvad_result = handler.vad_file(\"audio.wav\")\nprint(vad_result)\n# Add punctuation to a text\npunctuation_text = handler.punc_result(\"this is a test\")\nprint(punctuation_text)\n# Perform emotion classification on an audio file\nemotion_result = handler.emo_classify_file(\"audio.wav\")\nprint(emotion_result)\n```\n\n## 4. Introduction to the dspeech.TTS\n\n### 4.1 Initialization\n\nTo initialize the `TTS` module, create a `TTS` handler object specifying the target device (CPU or GPU) and sample rate for generated audio.\n\n```python\nfrom dspeech import TTS\nimport torch\n\n# Initialize TTS handler\ntts_handler = TTS(\n device=\"cuda\", # Use \"cpu\" if no GPU is available\n target_sample_rate=24000 # Define target sample rate for output audio\n)\n```\n\nThe `device` parameter can be set to \"cuda\" for GPU usage or \"cpu\" for running on the CPU.\n\n### 4.2 Basic Voice Cloning\n\nIn basic voice cloning, you provide a reference audio and text, and the system generates a new speech that mimics the voice from the reference audio with the content of the provided text.\n\n```python\nimport torchaudio\n\n# Load reference audio using torchaudio\nref_audio, sample_rate = torchaudio.load(\"tests/a.wav\")\n\n# Clone voice based on reference audio and text\nr = tts_handler.clone(\n ref_audio=(ref_audio, sample_rate), # Reference audio in (Tensor, int) format or file path\n ref_text=\"Reference text\", # The transcription of the reference audio\n gen_text_batches=[\"Hello, my name is Xiao Ming\", \"I am an AI\", \"I can speak Chinese\"], # Text to generate speech for\n speed=1, # Speech speed (1 is normal speed)\n channel=-1, # Merge all channels (-1) or specify one channel\n remove_silence=True, # Option to remove silence from the reference audio\n wave_path=\"tests/tts_output.wav\", # Path to save generated audio\n spectrogram_path=\"tests/tts_output.png\", # Path to save spectrogram of generated audio\n concat=True # Whether to merge all generated audio into a single output file\n)\n```\n\n**Parameters:**\n- `ref_audio`: The reference audio in the format `(Tensor, int)` or as a file path.\n- `ref_text`: The transcription of the reference audio.\n- `gen_text_batches`: A list of text strings that you want to convert into speech.\n- `speed`: Adjusts the speed of the generated speech (default is 1, for normal speed).\n- `remove_silence`: Option to remove silence from the reference audio (boolean).\n- `wave_path`: Path to save the generated audio file.\n- `spectrogram_path`: Path to save the spectrogram image of the generated audio.\n\n### 4.3 Extracting Speaker Information**\n\nFor complex voice cloning with multiple speakers and emotions, you need to extract speaker information from a directory containing multiple audio files for different speakers and emotions.\n\nThe directory should have the following structure:\n\n```\n<path>/\n\u251c\u2500\u2500 speaker1/\n\u2502 \u251c\u2500\u2500 happy.wav\n\u2502 \u251c\u2500\u2500 happy.txt\n\u2502 \u251c\u2500\u2500 neutral.wav\n\u2502 \u251c\u2500\u2500 neutral.txt\n```\n\nEach subdirectory represents a speaker, and each audio file should have an accompanying text file.\n\n```python\n# Extract speaker information from the folder\nspk_info = tts_handler.get_speaker_info(\"tests/speaker\")\nprint(spk_info)\n```\n\nThis function returns a dictionary of speaker information, which will be used for advanced cloning tasks.\n\n### 4.4 Voice Cloning with Emotions**\n\nTo clone a voice with emotional expressions, you can use the `clone_with_emo` method. The generated text should contain emotional markers, e.g., `[[zhaosheng_angry]]`, where `zhaosheng` is the speaker and `angry` is the emotion.\n\n```python\nr = tts_handler.clone_with_emo(\n gen_text_batches=[\n \"[[zhaosheng_angry]] How could you talk to me like that? It's too much!\",\n \"[[zhaosheng_whisper]] Be careful, don't let anyone hear, it's a secret.\",\n \"[[zhaosheng_sad]] I'm really sad, things are out of my control.\"\n ],\n speaker_info=spk_info, # Dictionary of speaker information\n speed=1, # Speech speed\n channel=-1, # Merge all channels\n remove_silence=True, # Remove silence in the generated output\n wave_path=\"tests/tts_output_emo.wav\", # Path to save output audio with emotions\n spectrogram_path=\"tests/tts_output_emo.png\" # Path to save spectrogram with emotions\n)\n```\n\n### 4.5 Multi-Speaker and Multi-Emotion Dialogues\n\nFor generating dialogues between multiple speakers with different emotions, make sure the directory `tests/speaker` contains subdirectories for each speaker, and the corresponding audio and text files exist for each emotion.\n\n```python\n# Extract speaker information for multiple speakers\nspk_info = tts_handler.get_speaker_info(\"tests/speaker\")\n\n# Generate multi-speaker and multi-emotion dialogue\nr = tts_handler.clone_with_emo(\n gen_text_batches=[\n \"[[zhaosheng_angry]] How could you talk to me like that? It's too much!\",\n \"[[duanyibo_whisper]] Be careful, don't let anyone hear, it's a secret.\",\n \"[[zhaosheng_sad]] I'm really sad, things are out of my control.\"\n ],\n speaker_info=spk_info, # Speaker information extracted from directory\n speed=1, # Speech speed\n channel=-1, # Merge all channels\n remove_silence=True, # Remove silence from the reference audio\n wave_path=\"tests/tts_output_emo.wav\", # Path to save generated audio\n spectrogram_path=\"tests/tts_output_emo.png\" # Path to save generated spectrogram\n)\n```\nThis method will generate a single audio file containing speech from multiple speakers with different emotional expressions.\n\n### 4.6 Output Files**\n\n- **Wave Path (`wave_path`)**: Specifies where to save the generated audio output. If `concat=True`, all `gen_text_batches` will be concatenated into one audio file.\n- **Spectrogram Path (`spectrogram_path`)**: Specifies where to save the spectrogram image of the generated speech. This is useful for visual analysis of the audio.\n\n\n## Command-line Interface\nDSpeech provides a command-line interface for quick and easy access to its functionalities. To see the available commands, run:\n```bash\ndspeech help\n\nDSpeech: A Command-line Speech Processing Toolkit\nUsage: dspeech \nCommands:\n transcribe Transcribe an audio file\n vad Perform VAD on an audio file\n punc Add punctuation to a text\n emo Perform emotion classification on an audio file\nOptions:\n --model Model name (default: sensevoicesmall)\n --vad-model VAD model name (default: fsmn-vad)\n --punc-model Punctuation model name (default: ct-punc)\n --emo-model Emotion model name (default: emotion2vec_plus_large)\n --device Device to run the models on (default: cuda)\n --file Audio file path for transcribing, VAD, or emotion classification\n --text Text to process with punctuation model\n --start Start time in seconds for processing audio files (default: 0)\n --end End time in seconds for processing audio files (default: end of file)\n --sample-rate Sample rate of the audio file (default: 16000)\nExample: dspeech transcribe --file audio.wav\n\n```\n\n### Usage Examples\n- **Transcribe an audio file**:\n ```bash\n dspeech transcribe --file audio.wav\n ```\n- **Perform VAD on an audio file**:\n ```bash\n dspeech vad --file audio.wav\n ```\n- **Add punctuation to a text**:\n ```bash\n dspeech punc --text \"this is a test\"\n ```\n- **Perform emotion classification on an audio file**:\n ```bash\n dspeech emo --file audio.wav\n ```\n\n## License\nDSpeech is licensed under the MIT License. See the LICENSE file for more details.\n\n",
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