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/language_model.py

@@ -18,6 +18,15 @@ logger = logging.getLogger(__name__)
 console = Console()
 
 
+WHISPER_LANGUAGE_TO_LLM_LANGUAGE = {
+    "en": "english",
+    "fr": "french",
+    "es": "spanish",
+    "zh": "chinese",
+    "ja": "japanese",
+    "ko": "korean",
+}
+
 class LanguageModelHandler(BaseHandler):
     """
     Handles the language model part.
@@ -69,7 +78,7 @@ class LanguageModelHandler(BaseHandler):
     def warmup(self):
         logger.info(f"Warming up {self.__class__.__name__}")
 
-        dummy_input_text = "Write me a poem about Machine Learning."
+        dummy_input_text = "Repeat the word 'home'."
         dummy_chat = [{"role": self.user_role, "content": dummy_input_text}]
         warmup_gen_kwargs = {
             "min_new_tokens": self.gen_kwargs["min_new_tokens"],
@@ -103,6 +112,10 @@ class LanguageModelHandler(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})
         thread = Thread(
@@ -122,10 +135,10 @@ class LanguageModelHandler(BaseHandler):
                 printable_text += new_text
                 sentences = sent_tokenize(printable_text)
                 if len(sentences) > 1:
-                    yield (sentences[0])
+                    yield (sentences[0], language_code)
                     printable_text = new_text
 
         self.chat.append({"role": "assistant", "content": generated_text})
 
         # don't forget last sentence
-        yield printable_text
+        yield (printable_text, language_code)

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

@@ -14,7 +14,7 @@
 * [Usage](#usage)
   - [Docker Server approach](#docker-server)
   - [Server/Client approach](#serverclient-approach)
-  - [Local approach](#local-approach)
+  - [Local approach](#local-approach-running-on-mac)
 * [Command-line usage](#command-line-usage)
   - [Model parameters](#model-parameters)
   - [Generation parameters](#generation-parameters)
@@ -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>
+   ```
 
-### 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):
     """
@@ -19,15 +29,19 @@ class LightningWhisperSTTHandler(BaseHandler):
     def setup(
         self,
         model_name="distil-large-v3",
-        device="cuda",
+        device="mps",
         torch_dtype="float16",
         compile_mode=None,
+        language=None,
         gen_kwargs={},
     ):
         if len(model_name.split("/")) > 1:
             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):
@@ -46,10 +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)

STT/whisper_stt_handler.py

@@ -1,10 +1,10 @@
 from time import perf_counter
 from transformers import (
-    AutoModelForSpeechSeq2Seq,
     AutoProcessor,
+    AutoModelForSpeechSeq2Seq
 )
 import torch
-
+from copy import copy
 from baseHandler import BaseHandler
 from rich.console import Console
 import logging
@@ -12,6 +12,15 @@ import logging
 logger = logging.getLogger(__name__)
 console = Console()
 
+SUPPORTED_LANGUAGES = [
+    "en",
+    "fr",
+    "es",
+    "zh",
+    "ja",
+    "ko",
+]
+
 
 class WhisperSTTHandler(BaseHandler):
     """
@@ -24,12 +33,18 @@ class WhisperSTTHandler(BaseHandler):
         device="cuda",
         torch_dtype="float16",
         compile_mode=None,
+        language=None,
         gen_kwargs={},
     ):
         self.device = device
         self.torch_dtype = getattr(torch, torch_dtype)
         self.compile_mode = compile_mode
         self.gen_kwargs = gen_kwargs
+        if language == 'auto':
+            language = None
+        self.last_language = language
+        if self.last_language is not None:
+            self.gen_kwargs["language"] = self.last_language
 
         self.processor = AutoProcessor.from_pretrained(model_name)
         self.model = AutoModelForSpeechSeq2Seq.from_pretrained(
@@ -102,11 +117,24 @@ class WhisperSTTHandler(BaseHandler):
 
         input_features = self.prepare_model_inputs(spoken_prompt)
         pred_ids = self.model.generate(input_features, **self.gen_kwargs)
+        language_code = self.processor.tokenizer.decode(pred_ids[0, 1])[2:-2]  # remove "<|" and "|>"
+
+        if language_code not in SUPPORTED_LANGUAGES:  # reprocess with the last language
+            logger.warning("Whisper detected unsupported language:", language_code)
+            gen_kwargs = copy(self.gen_kwargs)
+            gen_kwargs['language'] = self.last_language
+            language_code = self.last_language
+            pred_ids = self.model.generate(input_features, **gen_kwargs)
+        else:
+            self.last_language = language_code
+        
         pred_text = self.processor.batch_decode(
             pred_ids, skip_special_tokens=True, decode_with_timestamps=False
         )[0]
+        language_code = self.processor.tokenizer.decode(pred_ids[0, 1])[2:-2] # remove "<|" and "|>"
 
         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)

TTS/melo_handler.py

@@ -10,21 +10,44 @@ logger = logging.getLogger(__name__)
 
 console = Console()
 
+WHISPER_LANGUAGE_TO_MELO_LANGUAGE = {
+    "en": "EN_NEWEST",
+    "fr": "FR",
+    "es": "ES",
+    "zh": "ZH",
+    "ja": "JP",
+    "ko": "KR",
+}
+
+WHISPER_LANGUAGE_TO_MELO_SPEAKER = {
+    "en": "EN-Newest",
+    "fr": "FR",
+    "es": "ES",
+    "zh": "ZH",
+    "ja": "JP",
+    "ko": "KR",
+}
+
 
 class MeloTTSHandler(BaseHandler):
     def setup(
         self,
         should_listen,
         device="mps",
-        language="EN_NEWEST",
-        speaker_to_id="EN-Newest",
+        language="en",
+        speaker_to_id="en",
         gen_kwargs={},  # Unused
         blocksize=512,
     ):
         self.should_listen = should_listen
         self.device = device
-        self.model = TTS(language=language, device=device)
-        self.speaker_id = self.model.hps.data.spk2id[speaker_to_id]
+        self.language = language
+        self.model = TTS(
+            language=WHISPER_LANGUAGE_TO_MELO_LANGUAGE[self.language], device=device
+        )
+        self.speaker_id = self.model.hps.data.spk2id[
+            WHISPER_LANGUAGE_TO_MELO_SPEAKER[speaker_to_id]
+        ]
         self.blocksize = blocksize
         self.warmup()
 
@@ -33,7 +56,28 @@ class MeloTTSHandler(BaseHandler):
         _ = self.model.tts_to_file("text", self.speaker_id, quiet=True)
 
     def process(self, llm_sentence):
+        language_code = None
+
+        if isinstance(llm_sentence, tuple):
+            llm_sentence, language_code = llm_sentence
+
         console.print(f"[green]ASSISTANT: {llm_sentence}")
+
+        if language_code is not None and self.language != language_code:
+            try:
+                self.model = TTS(
+                    language=WHISPER_LANGUAGE_TO_MELO_LANGUAGE[language_code],
+                    device=self.device,
+                )
+                self.speaker_id = self.model.hps.data.spk2id[
+                    WHISPER_LANGUAGE_TO_MELO_SPEAKER[language_code]
+                ]
+                self.language = language_code
+            except KeyError:
+                console.print(
+                    f"[red]Language {language_code} not supported by Melo. Using {self.language} instead."
+                )
+
         if self.device == "mps":
             import time
 
@@ -44,7 +88,13 @@ class MeloTTSHandler(BaseHandler):
                 time.time() - start
             )  # Removing this line makes it fail more often. I'm looking into it.
 
-        audio_chunk = self.model.tts_to_file(llm_sentence, self.speaker_id, quiet=True)
+        try:
+            audio_chunk = self.model.tts_to_file(
+                llm_sentence, self.speaker_id, quiet=True
+            )
+        except (AssertionError, RuntimeError) as e:
+            logger.error(f"Error in MeloTTSHandler: {e}")
+            audio_chunk = np.array([])
         if len(audio_chunk) == 0:
             self.should_listen.set()
             return

TTS/parler_handler.py

@@ -147,6 +147,9 @@ class ParlerTTSHandler(BaseHandler):
             )
 
     def process(self, llm_sentence):
+        if isinstance(llm_sentence, tuple):
+            llm_sentence, _ = llm_sentence
+            
         console.print(f"[green]ASSISTANT: {llm_sentence}")
         nb_tokens = len(self.prompt_tokenizer(llm_sentence).input_ids)
 

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/melo_tts_arguments.py

@@ -4,7 +4,7 @@ from dataclasses import dataclass, field
 @dataclass
 class MeloTTSHandlerArguments:
     melo_language: str = field(
-        default="EN_NEWEST",
+        default="en",
         metadata={
             "help": "The language of the text to be synthesized. Default is 'EN_NEWEST'."
         },
@@ -16,7 +16,7 @@ class MeloTTSHandlerArguments:
         },
     )
     melo_speaker_to_id: str = field(
-        default="EN-Newest",
+        default="en",
         metadata={
             "help": "Mapping of speaker names to speaker IDs. Default is ['EN-Newest']."
         },

arguments_classes/module_arguments.py

@@ -11,7 +11,7 @@ class ModuleArguments:
     mode: Optional[str] = field(
         default="socket",
         metadata={
-            "help": "The mode to run the pipeline in. Either 'local' or 'socket'. Default is 'local'."
+            "help": "The mode to run the pipeline in. Either 'local' or 'socket'. Default is 'socket'."
         },
     )
     local_mac_optimal_settings: bool = field(

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."
         },
     )

arguments_classes/whisper_stt_arguments.py

@@ -1,4 +1,5 @@
 from dataclasses import dataclass, field
+from typing import Optional
 
 
 @dataclass
@@ -51,9 +52,13 @@ class WhisperSTTHandlerArguments:
             "help": "The task to perform, typically 'transcribe' for transcription. Default is 'transcribe'."
         },
     )
-    stt_gen_language: str = field(
-        default="en",
+    language: Optional[str] = field(
+        default='en',
         metadata={
-            "help": "The language of the speech to transcribe. Default is 'en' for English."
+            "help": """The language for the conversation. 
+            Choose between 'en' (english), 'fr' (french), 'es' (spanish), 
+            'zh' (chinese), 'ko' (korean), 'ja' (japanese), or 'None'.
+            If using 'auto', the language is automatically detected and can
+            change during the conversation. Default is 'en'."""
         },
-    )
+    )

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 

s2s_pipeline.py

@@ -299,7 +299,6 @@ def main():
             setup_args=(should_listen,),
             setup_kwargs=vars(parler_tts_handler_kwargs),
         )
-
     elif module_kwargs.tts == "melo":
         try:
             from TTS.melo_handler import MeloTTSHandler