Instructions to use MoYoYoTech/Translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use MoYoYoTech/Translator with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MoYoYoTech/Translator", filename="moyoyo_asr_models/qwen2.5-1.5b-instruct-q5_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use MoYoYoTech/Translator with llama.cpp:
Install from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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 Studio
How to use MoYoYoTech/Translator with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MoYoYoTech/Translator to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MoYoYoTech/Translator to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MoYoYoTech/Translator to start chatting
- Pi
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-server -hf MoYoYoTech/Translator:Q5_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/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
- Hermes Agent new
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-server -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
- 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
david commited on
Commit ·
b2de29e
1
Parent(s): 02e7bde
update coroutine call
Browse files
transcribe/whisper_llm_serve.py
CHANGED
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@@ -5,7 +5,7 @@ import threading
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import time
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from logging import getLogger
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from typing import List, Optional, Iterator, Tuple, Any
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-
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import numpy as np
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# import wordninja
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from api_model import TransResult, Message
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@@ -40,7 +40,7 @@ class WhisperTranscriptionService(ServeClientBase):
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# 文本分隔符,根据语言设置
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self.text_separator = self._get_text_separator(language)
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-
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# 发送就绪状态
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self.send_ready_state()
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self._transcrible_analysis = None
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@@ -50,6 +50,8 @@ class WhisperTranscriptionService(ServeClientBase):
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self.translate_thread = self._start_thread(self._transcription_processing_loop)
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self.frame_processing_thread = self._start_thread(self._frame_processing_loop)
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def _start_thread(self, target_function) -> threading.Thread:
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"""启动守护线程执行指定函数"""
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@@ -100,9 +102,13 @@ class WhisperTranscriptionService(ServeClientBase):
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"""应用语音活动检测来优化音频缓冲区"""
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with self.lock:
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if self.frames_np is not None:
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frame = self.frames_np.copy()
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processed_audio = self._translate_pipe.voice_detect(frame.tobytes())
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self.frames_np = np.frombuffer(processed_audio.audio, dtype=np.float32).copy()
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def _update_audio_buffer(self, offset: int) -> None:
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"""从音频缓冲区中移除已处理的部分"""
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@@ -204,8 +210,7 @@ class WhisperTranscriptionService(ServeClientBase):
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time.sleep(0.2)
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continue
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logger.debug(f"🥤 Buffer Length: {len(audio_buffer)/self.sample_rate:.2f} ")
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-
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# save_to_wave(f"dev-{c}.wav", audio_buffer)
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# try:
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segments = self._transcribe_audio(audio_buffer)
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@@ -255,7 +260,7 @@ class WhisperTranscriptionService(ServeClientBase):
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try:
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message = Message(result=result, request_id=self.client_uid).model_dump_json(by_alias=True)
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coro = self.websocket.send_text(message)
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asyncio.
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except RuntimeError:
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self.stop()
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except Exception as e:
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import time
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from logging import getLogger
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from typing import List, Optional, Iterator, Tuple, Any
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import asyncio
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import numpy as np
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# import wordninja
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from api_model import TransResult, Message
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# 文本分隔符,根据语言设置
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self.text_separator = self._get_text_separator(language)
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self.loop = asyncio.get_event_loop()
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# 发送就绪状态
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self.send_ready_state()
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self._transcrible_analysis = None
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self.translate_thread = self._start_thread(self._transcription_processing_loop)
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self.frame_processing_thread = self._start_thread(self._frame_processing_loop)
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# self._c = 0
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def _start_thread(self, target_function) -> threading.Thread:
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"""启动守护线程执行指定函数"""
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"""应用语音活动检测来优化音频缓冲区"""
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with self.lock:
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if self.frames_np is not None:
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self._c+= 1
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frame = self.frames_np.copy()
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processed_audio = self._translate_pipe.voice_detect(frame.tobytes())
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self.frames_np = np.frombuffer(processed_audio.audio, dtype=np.float32).copy()
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# if len(frame) > self.sample_rate:
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# save_to_wave(f"{self._c}-org.wav", frame)
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# save_to_wave(f"{self._c}-vad.wav", self.frames_np)
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def _update_audio_buffer(self, offset: int) -> None:
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"""从音频缓冲区中移除已处理的部分"""
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time.sleep(0.2)
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continue
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logger.debug(f"🥤 Buffer Length: {len(audio_buffer)/self.sample_rate:.2f} ")
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# try:
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segments = self._transcribe_audio(audio_buffer)
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try:
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message = Message(result=result, request_id=self.client_uid).model_dump_json(by_alias=True)
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coro = self.websocket.send_text(message)
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asyncio.run_coroutine_threadsafe(coro, self.loop)
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except RuntimeError:
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self.stop()
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except Exception as e:
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