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 Settings
- 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 ·
352d0e5
1
Parent(s): 9608722
fix vad bug
Browse files
transcribe/pipelines/pipe_vad.py
CHANGED
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@@ -42,10 +42,12 @@ class VadPipe(BasePipe):
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source_audio = in_data.source_audio
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source_audio = np.frombuffer(source_audio, dtype=np.float32)
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send_audio = b""
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speech_timestamps = get_speech_timestamps(source_audio, self.model.silero_vad, sampling_rate=16000)
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if speech_timestamps:
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send_audio = collect_chunks(speech_timestamps, torch.Tensor(source_audio))
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send_audio = send_audio.numpy()
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# send_audio = self.reduce_noise(send_audio).tobytes()
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in_data.source_audio = b""
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return in_data
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source_audio = in_data.source_audio
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source_audio = np.frombuffer(source_audio, dtype=np.float32)
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send_audio = b""
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speech_timestamps = get_speech_timestamps(torch.Tensor(source_audio), self.model.silero_vad, sampling_rate=16000)
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if speech_timestamps:
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send_audio = collect_chunks(speech_timestamps, torch.Tensor(source_audio))
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send_audio = send_audio.numpy()
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in_data.audio = send_audio
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# send_audio = self.reduce_noise(send_audio).tobytes()
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in_data.source_audio = b""
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return in_data
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transcribe/whisper_llm_serve.py
CHANGED
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@@ -68,9 +68,8 @@ class PyWhiperCppServe(ServeClientBase):
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with self.lock:
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frame = self.frames_np.copy()
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item = self._translate_pipes.voice_detect(frame.tobytes())
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self.frames_np = frame_np.copy()
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def get_frame_from_queue(self,):
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@@ -103,6 +102,7 @@ class PyWhiperCppServe(ServeClientBase):
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item = self._translate_pipes.transcrible(audio_buffer.tobytes(), self.language)
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segments = item.segments
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log_block("Whisper transcrible time", f"{(time.perf_counter() - start_time):.3f}", "s")
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return segments
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@@ -184,6 +184,7 @@ class PyWhiperCppServe(ServeClientBase):
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if last_cut_index:
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self.update_audio_buffer(last_cut_index)
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# 句子或者短句的提交
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self._segment_manager.handle(left_string).commit(is_end_sentence)
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self._segment_manager.handle(right_string)
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with self.lock:
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frame = self.frames_np.copy()
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item = self._translate_pipes.voice_detect(frame.tobytes())
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frame_np = np.frombuffer(item.audio, dtype=np.float32)
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self.frames_np = frame_np.copy()
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def get_frame_from_queue(self,):
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item = self._translate_pipes.transcrible(audio_buffer.tobytes(), self.language)
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segments = item.segments
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log_block("Whisper transcrible out", f"{''.join(seg.text for seg in segments)}", "")
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log_block("Whisper transcrible time", f"{(time.perf_counter() - start_time):.3f}", "s")
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return segments
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if last_cut_index:
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self.update_audio_buffer(last_cut_index)
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# 句子或者短句的提交
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log_block("Whisper string lock ", f"{left_string}",)
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self._segment_manager.handle(left_string).commit(is_end_sentence)
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self._segment_manager.handle(right_string)
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