Instructions to use nvidia/magpie_tts_multilingual_357m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/magpie_tts_multilingual_357m with NeMo:
# tag did not correspond to a valid NeMo domain.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use nvidia/magpie_tts_multilingual_357m 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 nvidia/magpie_tts_multilingual_357m:F16 # Run inference directly in the terminal: llama cli -hf nvidia/magpie_tts_multilingual_357m:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nvidia/magpie_tts_multilingual_357m:F16 # Run inference directly in the terminal: llama cli -hf nvidia/magpie_tts_multilingual_357m:F16
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 nvidia/magpie_tts_multilingual_357m:F16 # Run inference directly in the terminal: ./llama-cli -hf nvidia/magpie_tts_multilingual_357m:F16
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 nvidia/magpie_tts_multilingual_357m:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf nvidia/magpie_tts_multilingual_357m:F16
Use Docker
docker model run hf.co/nvidia/magpie_tts_multilingual_357m:F16
- LM Studio
- Jan
- Ollama
How to use nvidia/magpie_tts_multilingual_357m with Ollama:
ollama run hf.co/nvidia/magpie_tts_multilingual_357m:F16
- Unsloth Studio
How to use nvidia/magpie_tts_multilingual_357m 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 nvidia/magpie_tts_multilingual_357m 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 nvidia/magpie_tts_multilingual_357m to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nvidia/magpie_tts_multilingual_357m to start chatting
- Docker Model Runner
How to use nvidia/magpie_tts_multilingual_357m with Docker Model Runner:
docker model run hf.co/nvidia/magpie_tts_multilingual_357m:F16
- Lemonade
How to use nvidia/magpie_tts_multilingual_357m with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nvidia/magpie_tts_multilingual_357m:F16
Run and chat with the model
lemonade run user.magpie_tts_multilingual_357m-F16
List all available models
lemonade list
- Atomic Chat
docs(readme): add NIM Try via API (Magpie TTS Multilingual)
#5
by Amargolin - opened
README.md
CHANGED
|
@@ -286,6 +286,80 @@ When `--run_evaluation` is enabled, the following metrics are computed:
|
|
| 286 |
| **RTF** | Real-time factor (processing time / audio duration) |
|
| 287 |
|
| 288 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 289 |
## Software Integration:
|
| 290 |
**Runtime Engine(s):** NeMo Framework 25.11
|
| 291 |
|
|
|
|
| 286 |
| **RTF** | Real-time factor (processing time / audio duration) |
|
| 287 |
|
| 288 |
|
| 289 |
+
## Try via API — No Setup Required
|
| 290 |
+
|
| 291 |
+
Synthesize speech using the hosted NVIDIA NIM API on [Magpie TTS Multilingual](https://build.nvidia.com/nvidia/magpie-tts-multilingual) — no local GPU, Docker, or checkpoint download required.
|
| 292 |
+
|
| 293 |
+
**1. Get a free API key:** Open [Magpie TTS Multilingual](https://build.nvidia.com/nvidia/magpie-tts-multilingual) and choose **Get API Key**.
|
| 294 |
+
|
| 295 |
+
**2. Install the Riva client:**
|
| 296 |
+
|
| 297 |
+
```bash
|
| 298 |
+
pip install nvidia-riva-client
|
| 299 |
+
```
|
| 300 |
+
|
| 301 |
+
**3. Synthesize speech to a WAV file:**
|
| 302 |
+
|
| 303 |
+
```python
|
| 304 |
+
import wave
|
| 305 |
+
|
| 306 |
+
import riva.client
|
| 307 |
+
from riva.client.proto.riva_audio_pb2 import AudioEncoding
|
| 308 |
+
|
| 309 |
+
auth = riva.client.Auth(
|
| 310 |
+
uri="grpc.nvcf.nvidia.com:443",
|
| 311 |
+
use_ssl=True,
|
| 312 |
+
metadata_args=[
|
| 313 |
+
["function-id", "877104f7-e885-42b9-8de8-f6e4c6303969"],
|
| 314 |
+
["authorization", "Bearer nvapi-YOUR_API_KEY"],
|
| 315 |
+
],
|
| 316 |
+
)
|
| 317 |
+
|
| 318 |
+
service = riva.client.SpeechSynthesisService(auth)
|
| 319 |
+
|
| 320 |
+
sample_rate_hz = 22050
|
| 321 |
+
resp = service.synthesize(
|
| 322 |
+
"Hello from the Magpie multilingual hosted API.",
|
| 323 |
+
"Magpie-Multilingual.EN-US.Sofia",
|
| 324 |
+
"en-US",
|
| 325 |
+
sample_rate_hz=sample_rate_hz,
|
| 326 |
+
encoding=AudioEncoding.LINEAR_PCM,
|
| 327 |
+
)
|
| 328 |
+
|
| 329 |
+
with wave.open("out.wav", "wb") as wf:
|
| 330 |
+
wf.setnchannels(1)
|
| 331 |
+
wf.setsampwidth(2)
|
| 332 |
+
wf.setframerate(sample_rate_hz)
|
| 333 |
+
wf.writeframesraw(resp.audio)
|
| 334 |
+
```
|
| 335 |
+
|
| 336 |
+
**Or use the CLI (list voices, then synthesize):**
|
| 337 |
+
|
| 338 |
+
```bash
|
| 339 |
+
git clone https://github.com/nvidia-riva/python-clients.git
|
| 340 |
+
export NVIDIA_API_KEY="nvapi-YOUR_API_KEY"
|
| 341 |
+
|
| 342 |
+
python python-clients/scripts/tts/talk.py \
|
| 343 |
+
--server grpc.nvcf.nvidia.com:443 --use-ssl \
|
| 344 |
+
--metadata function-id "877104f7-e885-42b9-8de8-f6e4c6303969" \
|
| 345 |
+
--metadata authorization "Bearer $NVIDIA_API_KEY" \
|
| 346 |
+
--list-voices
|
| 347 |
+
|
| 348 |
+
python python-clients/scripts/tts/talk.py \
|
| 349 |
+
--server grpc.nvcf.nvidia.com:443 --use-ssl \
|
| 350 |
+
--metadata function-id "877104f7-e885-42b9-8de8-f6e4c6303969" \
|
| 351 |
+
--metadata authorization "Bearer $NVIDIA_API_KEY" \
|
| 352 |
+
--text "Hello from Magpie." \
|
| 353 |
+
--voice "Magpie-Multilingual.EN-US.Sofia" \
|
| 354 |
+
--language-code en-US \
|
| 355 |
+
--sample-rate-hz 22050 \
|
| 356 |
+
-o out.wav
|
| 357 |
+
```
|
| 358 |
+
|
| 359 |
+
> **Note:** Voice IDs and supported sample rates follow the deployed NIM. Use `--list-voices` against this endpoint, or see the **API Reference** on the [Magpie TTS Multilingual](https://build.nvidia.com/nvidia/magpie-tts-multilingual) page.
|
| 360 |
+
|
| 361 |
+
|
| 362 |
+
|
| 363 |
## Software Integration:
|
| 364 |
**Runtime Engine(s):** NeMo Framework 25.11
|
| 365 |
|