Add support for a docker container for Jetson boards

Mac multi language

Dockerfile.arm64

@@ -0,0 +1,13 @@
+FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
+
+ENV PYTHONUNBUFFERED 1
+
+WORKDIR /usr/src/app
+
+# Install packages
+RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .

LLM/mlx_language_model.py

@@ -9,6 +9,14 @@ logger = logging.getLogger(__name__)
 
 console = Console()
 
+WHISPER_LANGUAGE_TO_LLM_LANGUAGE = {
+    "en": "english",
+    "fr": "french",
+    "es": "spanish",
+    "zh": "chinese",
+    "ja": "japanese",
+    "ko": "korean",
+}
 
 class MLXLanguageModelHandler(BaseHandler):
     """
@@ -61,6 +69,11 @@ class MLXLanguageModelHandler(BaseHandler):
 
     def process(self, prompt):
         logger.debug("infering language model...")
+        language_code = None
+
+        if isinstance(prompt, tuple):
+            prompt, language_code = prompt
+            prompt = f"Please reply to my message in {WHISPER_LANGUAGE_TO_LLM_LANGUAGE[language_code]}. " + prompt
 
         self.chat.append({"role": self.user_role, "content": prompt})
 
@@ -86,9 +99,9 @@ class MLXLanguageModelHandler(BaseHandler):
             output += t
             curr_output += t
             if curr_output.endswith((".", "?", "!", "<|end|>")):
-                yield curr_output.replace("<|end|>", "")
+                yield (curr_output.replace("<|end|>", ""), language_code)
                 curr_output = ""
         generated_text = output.replace("<|end|>", "")
         torch.mps.empty_cache()
 
-        self.chat.append({"role": "assistant", "content": generated_text})
+        self.chat.append({"role": "assistant", "content": generated_text})

README.md

@@ -79,27 +79,28 @@ https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install
 
 ### Server/Client Approach
 
-To run the pipeline on the server:
-```bash
-python s2s_pipeline.py --recv_host 0.0.0.0 --send_host 0.0.0.0
-```
+1. Run the pipeline on the server:
+   ```bash
+   python s2s_pipeline.py --recv_host 0.0.0.0 --send_host 0.0.0.0
+   ```
 
-Then run the client locally to handle sending microphone input and receiving generated audio:
-```bash
-python listen_and_play.py --host <IP address of your server>
-```
+2. Run the client locally to handle microphone input and receive generated audio:
+   ```bash
+   python listen_and_play.py --host <IP address of your server>
+   ```
 
-### Local approach (running on Mac)
-To run on mac, we recommend setting the flag `--local_mac_optimal_settings`:
-```bash
-python s2s_pipeline.py --local_mac_optimal_settings
-```
+### Local Approach (Mac)
 
-You can also pass `--device mps` to have all the models set to device mps.
-The local mac optimal settings set the mode to be local as explained above and change the models to:
-- LightningWhisperMLX
-- MLX LM
-- MeloTTS
+1. For optimal settings on Mac:
+   ```bash
+   python s2s_pipeline.py --local_mac_optimal_settings
+   ```
+
+This setting:
+   - Adds `--device mps` to use MPS for all models.
+     - Sets LightningWhisperMLX for STT
+     - Sets MLX LM for language model
+     - Sets MeloTTS for TTS
 
 ### Recommended usage with Cuda
 
@@ -117,6 +118,57 @@ python s2s_pipeline.py \
 
 For the moment, modes capturing CUDA Graphs are not compatible with streaming Parler-TTS (`reduce-overhead`, `max-autotune`).
 
+
+### Multi-language Support
+
+The pipeline supports multiple languages, allowing for automatic language detection or specific language settings. Here are examples for both local (Mac) and server setups:
+
+#### With the server version:
+
+
+For automatic language detection:
+
+```bash
+python s2s_pipeline.py \
+    --stt_model_name large-v3 \
+    --language zh \
+    --mlx_lm_model_name mlx-community/Meta-Llama-3.1-8B-Instruct \
+```
+
+Or for one language in particular, chinese in this example
+
+```bash
+python s2s_pipeline.py \
+    --stt_model_name large-v3 \
+    --language zh \
+    --mlx_lm_model_name mlx-community/Meta-Llama-3.1-8B-Instruct \
+```
+
+#### Local Mac Setup
+
+For automatic language detection:
+
+```bash
+python s2s_pipeline.py \
+    --local_mac_optimal_settings \
+    --device mps \
+    --stt_model_name large-v3 \
+    --language zh \
+    --mlx_lm_model_name mlx-community/Meta-Llama-3.1-8B-Instruct-4bit \
+```
+
+Or for one language in particular, chinese in this example
+
+```bash
+python s2s_pipeline.py \
+    --local_mac_optimal_settings \
+    --device mps \
+    --stt_model_name large-v3 \
+    --language zh \
+    --mlx_lm_model_name mlx-community/Meta-Llama-3.1-8B-Instruct-4bit \
+```
+
+
 ## Command-line Usage
 
 ### Model Parameters

STT/lightning_whisper_mlx_handler.py

@@ -4,12 +4,22 @@ from baseHandler import BaseHandler
 from lightning_whisper_mlx import LightningWhisperMLX
 import numpy as np
 from rich.console import Console
+from copy import copy
 import torch
 
 logger = logging.getLogger(__name__)
 
 console = Console()
 
+SUPPORTED_LANGUAGES = [
+    "en",
+    "fr",
+    "es",
+    "zh",
+    "ja",
+    "ko",
+]
+
 
 class LightningWhisperSTTHandler(BaseHandler):
     """
@@ -19,7 +29,7 @@ class LightningWhisperSTTHandler(BaseHandler):
     def setup(
         self,
         model_name="distil-large-v3",
-        device="cuda",
+        device="mps",
         torch_dtype="float16",
         compile_mode=None,
         language=None,
@@ -29,6 +39,9 @@ class LightningWhisperSTTHandler(BaseHandler):
             model_name = model_name.split("/")[-1]
         self.device = device
         self.model = LightningWhisperMLX(model=model_name, batch_size=6, quant=None)
+        self.start_language = language
+        self.last_language = language
+
         self.warmup()
 
     def warmup(self):
@@ -47,10 +60,26 @@ class LightningWhisperSTTHandler(BaseHandler):
         global pipeline_start
         pipeline_start = perf_counter()
 
-        pred_text = self.model.transcribe(spoken_prompt)["text"].strip()
+        if self.start_language != 'auto':
+            transcription_dict = self.model.transcribe(spoken_prompt, language=self.start_language)
+        else:
+            transcription_dict = self.model.transcribe(spoken_prompt)
+            language_code = transcription_dict["language"]
+            if language_code not in SUPPORTED_LANGUAGES:
+                logger.warning(f"Whisper detected unsupported language: {language_code}")
+                if self.last_language in SUPPORTED_LANGUAGES:  # reprocess with the last language
+                    transcription_dict = self.model.transcribe(spoken_prompt, language=self.last_language)
+                else:
+                    transcription_dict = {"text": "", "language": "en"}
+            else:
+                self.last_language = language_code
+
+        pred_text = transcription_dict["text"].strip()
+        language_code = transcription_dict["language"]
         torch.mps.empty_cache()
 
         logger.debug("finished whisper inference")
         console.print(f"[yellow]USER: {pred_text}")
+        logger.debug(f"Language Code Whisper: {language_code}")
 
-        yield pred_text
+        yield (pred_text, language_code)

VAD/vad_handler.py

@@ -86,3 +86,7 @@ class VADHandler(BaseHandler):
                         )
                     array = enhanced.numpy().squeeze()
                 yield array
+
+    @property
+    def min_time_to_debug(self):
+        return 0.00001

arguments_classes/vad_arguments.py

@@ -42,6 +42,6 @@ class VADHandlerArguments:
     audio_enhancement: bool = field(
         default=False,
         metadata={
-            "help": "improves sound quality by applying techniques like noise reduction, equalization, and echo cancellation. Default is True."
+            "help": "improves sound quality by applying techniques like noise reduction, equalization, and echo cancellation. Default is False."
         },
     )

baseHandler.py

@@ -36,7 +36,8 @@ class BaseHandler:
             start_time = perf_counter()
             for output in self.process(input):
                 self._times.append(perf_counter() - start_time)
-                logger.debug(f"{self.__class__.__name__}: {self.last_time: .3f} s")
+                if self.last_time > self.min_time_to_debug:
+                    logger.debug(f"{self.__class__.__name__}: {self.last_time: .3f} s")
                 self.queue_out.put(output)
                 start_time = perf_counter()
 
@@ -46,6 +47,10 @@ class BaseHandler:
     @property
     def last_time(self):
         return self._times[-1]
+    
+    @property
+    def min_time_to_debug(self):
+        return 0.001
 
     def cleanup(self):
         pass

docker-compose.yml

@@ -4,6 +4,7 @@ services:
   pipeline:
     build:
       context: .
+      dockerfile: ${DOCKERFILE:-Dockerfile}
     command: 
       - python3 
       - s2s_pipeline.py