Instructions to use MoYoYoTech/Translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use MoYoYoTech/Translator with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MoYoYoTech/Translator:Q5_0
# Run inference directly in the terminal:
llama cli -hf MoYoYoTech/Translator:Q5_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MoYoYoTech/Translator:Q5_0
# Run inference directly in the terminal:
llama cli -hf MoYoYoTech/Translator:Q5_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf MoYoYoTech/Translator:Q5_0
# Run inference directly in the terminal:
./llama-cli -hf MoYoYoTech/Translator:Q5_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf MoYoYoTech/Translator:Q5_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MoYoYoTech/Translator:Q5_0

Use Docker

docker model run hf.co/MoYoYoTech/Translator:Q5_0

LM Studio
Jan
Ollama
How to use MoYoYoTech/Translator with Ollama:
```
ollama run hf.co/MoYoYoTech/Translator:Q5_0
```
Unsloth Desktop

How to use MoYoYoTech/Translator with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MoYoYoTech/Translator:Q5_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MoYoYoTech/Translator:Q5_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use MoYoYoTech/Translator with Docker Model Runner:
```
docker model run hf.co/MoYoYoTech/Translator:Q5_0
```

Lemonade

How to use MoYoYoTech/Translator with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MoYoYoTech/Translator:Q5_0

Run and chat with the model

lemonade run user.Translator-Q5_0

List all available models

lemonade list

Hermes Agent

How to use MoYoYoTech/Translator with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MoYoYoTech/Translator:Q5_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default MoYoYoTech/Translator:Q5_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MoYoYoTech/Translator with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MoYoYoTech/Translator:Q5_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "MoYoYoTech/Translator:Q5_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

daihui.zhang commited on Apr 22, 2025

Commit

bdb9da4

1 Parent(s): d3badad

fix bug of loss segemnts

Browse files

Files changed (2) hide show

config.py +1 -1
transcribe/pipelines/pipe_vad.py +3 -3

config.py CHANGED Viewed

@@ -21,7 +21,7 @@ console_handler.setFormatter(console_formatter)
 logging.getLogger().addHandler(console_handler)
 # 文字输出长度阈值
-TEXT_THREHOLD = 16
 BASE_DIR = pathlib.Path(__file__).parent
 MODEL_DIR = BASE_DIR / "moyoyo_asr_models"

 logging.getLogger().addHandler(console_handler)
 # 文字输出长度阈值
+TEXT_THREHOLD = 6
 BASE_DIR = pathlib.Path(__file__).parent
 MODEL_DIR = BASE_DIR / "moyoyo_asr_models"

transcribe/pipelines/pipe_vad.py CHANGED Viewed

@@ -62,15 +62,15 @@ class VadPipe(BasePipe):
         if speech_data: # 表示有音频的变化点出现
             rel_start_frame, rel_end_frame = speech_data
-            if rel_start_frame and not rel_end_frame:
                 self._status = "START" # 语音开始
                 target_audio = source_audio[rel_start_frame:]
                 logging.debug("🫸 Speech start frame: {}".format(rel_start_frame))
-            elif not rel_start_frame and rel_end_frame:
                 self._status = "END" # 音频结束
                 target_audio = source_audio[:rel_end_frame]
                 logging.debug(" 🫷Speech ended, capturing audio up to frame: {}".format(rel_end_frame))
-            elif rel_start_frame and rel_end_frame:
                 self._status = 'END'
                 target_audio = source_audio[rel_start_frame:rel_end_frame]
                 logging.debug(" 🔄 Speech segment captured from frame {} to frame {}".format(rel_start_frame, rel_end_frame))

         if speech_data: # 表示有音频的变化点出现
             rel_start_frame, rel_end_frame = speech_data
+            if rel_start_frame is not None and rel_end_frame is None:
                 self._status = "START" # 语音开始
                 target_audio = source_audio[rel_start_frame:]
                 logging.debug("🫸 Speech start frame: {}".format(rel_start_frame))
+            elif rel_start_frame is None and rel_end_frame is not None:
                 self._status = "END" # 音频结束
                 target_audio = source_audio[:rel_end_frame]
                 logging.debug(" 🫷Speech ended, capturing audio up to frame: {}".format(rel_end_frame))
+            elif rel_start_frame is not None and rel_end_frame is not None:
                 self._status = 'END'
                 target_audio = source_audio[rel_start_frame:rel_end_frame]
                 logging.debug(" 🔄 Speech segment captured from frame {} to frame {}".format(rel_start_frame, rel_end_frame))