import argparse import io import os from time import time from typing import List import tempfile import uvicorn from fastapi import Depends, FastAPI, File, HTTPException, Query, Request, UploadFile, Body, Form from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse, RedirectResponse, StreamingResponse from PIL import Image from pydantic import BaseModel, field_validator from pydantic_settings import BaseSettings from slowapi import Limiter from slowapi.util import get_remote_address import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from IndicTransToolkit import IndicProcessor from logging_config import logger from tts_config import SPEED, ResponseFormat, config as tts_config from gemma_llm import LLMManager # from auth import get_api_key, settings as auth_settings import time from contextlib import asynccontextmanager from typing import Annotated, Any, OrderedDict, List import zipfile import soundfile as sf import torch from fastapi import Body, FastAPI, HTTPException, Response from parler_tts import ParlerTTSForConditionalGeneration from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed import numpy as np from config import SPEED, ResponseFormat, config from logger import logger import uvicorn import argparse from fastapi.responses import RedirectResponse, StreamingResponse import io import os import logging # Device setup if torch.cuda.is_available(): device = "cuda:0" logger.info("GPU will be used for inference") else: device = "cpu" logger.info("CPU will be used for inference") torch_dtype = torch.bfloat16 if device != "cpu" else torch.float32 # Check CUDA availability and version cuda_available = torch.cuda.is_available() cuda_version = torch.version.cuda if cuda_available else None if torch.cuda.is_available(): device_idx = torch.cuda.current_device() capability = torch.cuda.get_device_capability(device_idx) compute_capability_float = float(f"{capability[0]}.{capability[1]}") print(f"CUDA version: {cuda_version}") print(f"CUDA Compute Capability: {compute_capability_float}") else: print("CUDA is not available on this system.") class TTSModelManager: def __init__(self): self.model_tokenizer: OrderedDict[ str, tuple[ParlerTTSForConditionalGeneration, AutoTokenizer, AutoTokenizer] ] = OrderedDict() self.max_length = 50 def load_model( self, model_name: str ) -> tuple[ParlerTTSForConditionalGeneration, AutoTokenizer, AutoTokenizer]: logger.debug(f"Loading {model_name}...") start = time.perf_counter() model_name = "ai4bharat/indic-parler-tts" attn_implementation = "flash_attention_2" model = ParlerTTSForConditionalGeneration.from_pretrained( model_name, attn_implementation=attn_implementation ).to(device, dtype=torch_dtype) tokenizer = AutoTokenizer.from_pretrained(model_name) description_tokenizer = AutoTokenizer.from_pretrained(model.config.text_encoder._name_or_path) # Set pad tokens if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token if description_tokenizer.pad_token is None: description_tokenizer.pad_token = description_tokenizer.eos_token # TODO - temporary disable -torch.compile ''' # Update model configuration model.config.pad_token_id = tokenizer.pad_token_id # Update for deprecation: use max_batch_size instead of batch_size if hasattr(model.generation_config.cache_config, 'max_batch_size'): model.generation_config.cache_config.max_batch_size = 1 model.generation_config.cache_implementation = "static" ''' # Compile the model compile_mode = "default" #compile_mode = "reduce-overhead" model.forward = torch.compile(model.forward, mode=compile_mode) # Warmup warmup_inputs = tokenizer("Warmup text for compilation", return_tensors="pt", padding="max_length", max_length=self.max_length).to(device) model_kwargs = { "input_ids": warmup_inputs["input_ids"], "attention_mask": warmup_inputs["attention_mask"], "prompt_input_ids": warmup_inputs["input_ids"], "prompt_attention_mask": warmup_inputs["attention_mask"], } n_steps = 1 if compile_mode == "default" else 2 for _ in range(n_steps): _ = model.generate(**model_kwargs) logger.info( f"Loaded {model_name} with Flash Attention and compilation in {time.perf_counter() - start:.2f} seconds" ) return model, tokenizer, description_tokenizer def get_or_load_model( self, model_name: str ) -> tuple[ParlerTTSForConditionalGeneration, AutoTokenizer, AutoTokenizer]: if model_name not in self.model_tokenizer: logger.info(f"Model {model_name} isn't already loaded") if len(self.model_tokenizer) == config.max_models: logger.info("Unloading the oldest loaded model") del self.model_tokenizer[next(iter(self.model_tokenizer))] self.model_tokenizer[model_name] = self.load_model(model_name) return self.model_tokenizer[model_name] tts_model_manager = TTSModelManager() @asynccontextmanager async def lifespan(_: FastAPI): if not config.lazy_load_model: tts_model_manager.get_or_load_model(config.model) yield app = FastAPI( title="Dhwani API", description="AI Chat API supporting Indian languages", version="1.0.0", redirect_slashes=False, lifespan=lifespan ) def chunk_text(text, chunk_size): words = text.split() chunks = [] for i in range(0, len(words), chunk_size): chunks.append(' '.join(words[i:i + chunk_size])) return chunks @app.post("/v1/audio/speech") async def generate_audio( input: Annotated[str, Body()] = config.input, voice: Annotated[str, Body()] = config.voice, model: Annotated[str, Body()] = config.model, response_format: Annotated[ResponseFormat, Body(include_in_schema=False)] = config.response_format, speed: Annotated[float, Body(include_in_schema=False)] = SPEED, ) -> StreamingResponse: tts, tokenizer, description_tokenizer = tts_model_manager.get_or_load_model(model) if speed != SPEED: logger.warning( "Specifying speed isn't supported by this model. Audio will be generated with the default speed" ) start = time.perf_counter() chunk_size = 15 all_chunks = chunk_text(input, chunk_size) if len(all_chunks) <= chunk_size: desc_inputs = description_tokenizer(voice, return_tensors="pt", padding="max_length", max_length=tts_model_manager.max_length).to(device) prompt_inputs = tokenizer(input, return_tensors="pt", padding="max_length", max_length=tts_model_manager.max_length).to(device) input_ids = desc_inputs["input_ids"] attention_mask = desc_inputs["attention_mask"] prompt_input_ids = prompt_inputs["input_ids"] prompt_attention_mask = prompt_inputs["attention_mask"] generation = tts.generate( input_ids=input_ids, prompt_input_ids=prompt_input_ids, attention_mask=attention_mask, prompt_attention_mask=prompt_attention_mask ).to(torch.float32) audio_arr = generation.cpu().float().numpy().squeeze() else: all_descriptions = [voice] * len(all_chunks) description_inputs = description_tokenizer(all_descriptions, return_tensors="pt", padding=True).to(device) prompts = tokenizer(all_chunks, return_tensors="pt", padding=True).to(device) set_seed(0) generation = tts.generate( input_ids=description_inputs["input_ids"], attention_mask=description_inputs["attention_mask"], prompt_input_ids=prompts["input_ids"], prompt_attention_mask=prompts["attention_mask"], do_sample=True, return_dict_in_generate=True, ) chunk_audios = [] for i, audio in enumerate(generation.sequences): audio_data = audio[:generation.audios_length[i]].cpu().float().numpy().squeeze() chunk_audios.append(audio_data) audio_arr = np.concatenate(chunk_audios) device_str = str(device) logger.info( f"Took {time.perf_counter() - start:.2f} seconds to generate audio for {len(input.split())} words using {device_str.upper()}" ) audio_buffer = io.BytesIO() sf.write(audio_buffer, audio_arr, tts.config.sampling_rate, format=response_format) audio_buffer.seek(0) return StreamingResponse(audio_buffer, media_type=f"audio/{response_format}") def create_in_memory_zip(file_data): in_memory_zip = io.BytesIO() with zipfile.ZipFile(in_memory_zip, 'w') as zipf: for file_name, data in file_data.items(): zipf.writestr(file_name, data) in_memory_zip.seek(0) return in_memory_zip @app.post("/v1/audio/speech_batch") async def generate_audio_batch( input: Annotated[List[str], Body()] = config.input, voice: Annotated[List[str], Body()] = config.voice, model: Annotated[str, Body(include_in_schema=False)] = config.model, response_format: Annotated[ResponseFormat, Body()] = config.response_format, speed: Annotated[float, Body(include_in_schema=False)] = SPEED, ) -> StreamingResponse: tts, tokenizer, description_tokenizer = tts_model_manager.get_or_load_model(model) if speed != SPEED: logger.warning( "Specifying speed isn't supported by this model. Audio will be generated with the default speed" ) start = time.perf_counter() chunk_size = 15 all_chunks = [] all_descriptions = [] for i, text in enumerate(input): chunks = chunk_text(text, chunk_size) all_chunks.extend(chunks) all_descriptions.extend([voice[i]] * len(chunks)) description_inputs = description_tokenizer(all_descriptions, return_tensors="pt", padding=True).to(device) prompts = tokenizer(all_chunks, return_tensors="pt", padding=True).to(device) set_seed(0) generation = tts.generate( input_ids=description_inputs["input_ids"], attention_mask=description_inputs["attention_mask"], prompt_input_ids=prompts["input_ids"], prompt_attention_mask=prompts["attention_mask"], do_sample=True, return_dict_in_generate=True, ) audio_outputs = [] current_index = 0 for i, text in enumerate(input): chunks = chunk_text(text, chunk_size) chunk_audios = [] for j in range(len(chunks)): audio_arr = generation.sequences[current_index][:generation.audios_length[current_index]].cpu().float().numpy().squeeze() chunk_audios.append(audio_arr) current_index += 1 combined_audio = np.concatenate(chunk_audios) audio_outputs.append(combined_audio) file_data = {} for i, audio in enumerate(audio_outputs): file_name = f"out_{i}.{response_format}" audio_bytes = io.BytesIO() sf.write(audio_bytes, audio, tts.config.sampling_rate, format=response_format) audio_bytes.seek(0) file_data[file_name] = audio_bytes.read() in_memory_zip = create_in_memory_zip(file_data) logger.info( f"Took {time.perf_counter() - start:.2f} seconds to generate audio" ) return StreamingResponse(in_memory_zip, media_type="application/zip") # Supported language codes SUPPORTED_LANGUAGES = { "asm_Beng", "kas_Arab", "pan_Guru", "ben_Beng", "kas_Deva", "san_Deva", "brx_Deva", "mai_Deva", "sat_Olck", "doi_Deva", "mal_Mlym", "snd_Arab", "eng_Latn", "mar_Deva", "snd_Deva", "gom_Deva", "mni_Beng", "tam_Taml", "guj_Gujr", "mni_Mtei", "tel_Telu", "hin_Deva", "npi_Deva", "urd_Arab", "kan_Knda", "ory_Orya", "deu_Latn", "fra_Latn", "nld_Latn", "spa_Latn", "ita_Latn", "por_Latn", "rus_Cyrl", "pol_Latn" } class Settings(BaseSettings): llm_model_name: str = "google/gemma-3-4b-it" max_tokens: int = 512 host: str = "0.0.0.0" port: int = 7860 chat_rate_limit: str = "100/minute" speech_rate_limit: str = "5/minute" @field_validator("chat_rate_limit", "speech_rate_limit") def validate_rate_limit(cls, v): if not v.count("/") == 1 or not v.split("/")[0].isdigit(): raise ValueError("Rate limit must be in format 'number/period' (e.g., '5/minute')") return v class Config: env_file = ".env" settings = Settings() app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=False, allow_methods=["*"], allow_headers=["*"], ) limiter = Limiter(key_func=get_remote_address) app.state.limiter = limiter llm_manager = LLMManager(settings.llm_model_name) # Translation Manager and Model Manager DEVICE = "cuda" if torch.cuda.is_available() else "cpu" class TranslateManager: def __init__(self, src_lang, tgt_lang, device_type=DEVICE, use_distilled=True): self.device_type = device_type self.tokenizer, self.model = self.initialize_model(src_lang, tgt_lang, use_distilled) def initialize_model(self, src_lang, tgt_lang, use_distilled): if src_lang.startswith("eng") and not tgt_lang.startswith("eng"): model_name = "ai4bharat/indictrans2-en-indic-dist-200M" if use_distilled else "ai4bharat/indictrans2-en-indic-1B" elif not src_lang.startswith("eng") and tgt_lang.startswith("eng"): model_name = "ai4bharat/indictrans2-indic-en-dist-200M" if use_distilled else "ai4bharat/indictrans2-indic-en-1B" elif not src_lang.startswith("eng") and not tgt_lang.startswith("eng"): model_name = "ai4bharat/indictrans2-indic-indic-dist-320M" if use_distilled else "ai4bharat/indictrans2-indic-indic-1B" else: raise ValueError("Invalid language combination: English to English translation is not supported.") tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForSeq2SeqLM.from_pretrained( model_name, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2" ).to(self.device_type) return tokenizer, model class ModelManager: def __init__(self, device_type=DEVICE, use_distilled=True, is_lazy_loading=False): self.models: dict[str, TranslateManager] = {} self.device_type = device_type self.use_distilled = use_distilled self.is_lazy_loading = is_lazy_loading if not is_lazy_loading: self.preload_models() def preload_models(self): self.models['eng_indic'] = TranslateManager('eng_Latn', 'kan_Knda', self.device_type, self.use_distilled) self.models['indic_eng'] = TranslateManager('kan_Knda', 'eng_Latn', self.device_type, self.use_distilled) self.models['indic_indic'] = TranslateManager('kan_Knda', 'hin_Deva', self.device_type, self.use_distilled) def get_model(self, src_lang, tgt_lang) -> TranslateManager: if src_lang.startswith("eng") and not tgt_lang.startswith("eng"): key = 'eng_indic' elif not src_lang.startswith("eng") and tgt_lang.startswith("eng"): key = 'indic_eng' elif not src_lang.startswith("eng") and not tgt_lang.startswith("eng"): key = 'indic_indic' else: raise ValueError("Invalid language combination: English to English translation is not supported.") if key not in self.models: if self.is_lazy_loading: if key == 'eng_indic': self.models[key] = TranslateManager('eng_Latn', 'kan_Knda', self.device_type, self.use_distilled) elif key == 'indic_eng': self.models[key] = TranslateManager('kan_Knda', 'eng_Latn', self.device_type, self.use_distilled) elif key == 'indic_indic': self.models[key] = TranslateManager('kan_Knda', 'hin_Deva', self.device_type, self.use_distilled) else: raise ValueError(f"Model for {key} is not preloaded and lazy loading is disabled.") return self.models[key] ip = IndicProcessor(inference=True) model_manager = ModelManager() # Pydantic Models class ChatRequest(BaseModel): prompt: str src_lang: str = "kan_Knda" # Default to Kannada tgt_lang: str = "kan_Knda" # Default to Kannada @field_validator("prompt") def prompt_must_be_valid(cls, v): if len(v) > 1000: raise ValueError("Prompt cannot exceed 1000 characters") return v.strip() @field_validator("src_lang", "tgt_lang") def validate_language(cls, v): if v not in SUPPORTED_LANGUAGES: raise ValueError(f"Unsupported language code: {v}. Supported codes: {', '.join(SUPPORTED_LANGUAGES)}") return v class ChatResponse(BaseModel): response: str class TranslationRequest(BaseModel): sentences: List[str] src_lang: str tgt_lang: str class TranslationResponse(BaseModel): translations: List[str] # Dependency to get TranslateManager def get_translate_manager(src_lang: str, tgt_lang: str) -> TranslateManager: return model_manager.get_model(src_lang, tgt_lang) # Internal Translation Endpoint @app.post("/translate", response_model=TranslationResponse) async def translate(request: TranslationRequest, translate_manager: TranslateManager = Depends(get_translate_manager)): input_sentences = request.sentences src_lang = request.src_lang tgt_lang = request.tgt_lang if not input_sentences: raise HTTPException(status_code=400, detail="Input sentences are required") batch = ip.preprocess_batch(input_sentences, src_lang=src_lang, tgt_lang=tgt_lang) inputs = translate_manager.tokenizer( batch, truncation=True, padding="longest", return_tensors="pt", return_attention_mask=True, ).to(translate_manager.device_type) with torch.no_grad(): generated_tokens = translate_manager.model.generate( **inputs, use_cache=True, min_length=0, max_length=256, num_beams=5, num_return_sequences=1, ) with translate_manager.tokenizer.as_target_tokenizer(): generated_tokens = translate_manager.tokenizer.batch_decode( generated_tokens.detach().cpu().tolist(), skip_special_tokens=True, clean_up_tokenization_spaces=True, ) translations = ip.postprocess_batch(generated_tokens, lang=tgt_lang) return TranslationResponse(translations=translations) # Helper function to perform internal translation async def perform_internal_translation(sentences: List[str], src_lang: str, tgt_lang: str) -> List[str]: translate_manager = model_manager.get_model(src_lang, tgt_lang) request = TranslationRequest(sentences=sentences, src_lang=src_lang, tgt_lang=tgt_lang) response = await translate(request, translate_manager) return response.translations # API Endpoints @app.get("/v1/health") async def health_check(): return {"status": "healthy", "model": settings.llm_model_name} @app.get("/") async def home(): return RedirectResponse(url="/docs") @app.post("/v1/unload_all_models") async def unload_all_models(): try: logger.info("Starting to unload all models...") llm_manager.unload() logger.info("All models unloaded successfully") return {"status": "success", "message": "All models unloaded"} except Exception as e: logger.error(f"Error unloading models: {str(e)}") raise HTTPException(status_code=500, detail=f"Failed to unload models: {str(e)}") @app.post("/v1/load_all_models") async def load_all_models(): try: logger.info("Starting to load all models...") llm_manager.load() logger.info("All models loaded successfully") return {"status": "success", "message": "All models loaded"} except Exception as e: logger.error(f"Error loading models: {str(e)}") raise HTTPException(status_code=500, detail=f"Failed to unload models: {str(e)}") @app.post("/v1/translate", response_model=TranslationResponse) async def translate_endpoint(request: TranslationRequest): logger.info(f"Received translation request: {request.dict()}") try: translations = await perform_internal_translation( sentences=request.sentences, src_lang=request.src_lang, tgt_lang=request.tgt_lang ) logger.info(f"Translation successful: {translations}") return TranslationResponse(translations=translations) except Exception as e: logger.error(f"Unexpected error during translation: {str(e)}") raise HTTPException(status_code=500, detail=f"Translation failed: {str(e)}") @app.post("/v1/chat", response_model=ChatResponse) @limiter.limit(settings.chat_rate_limit) async def chat(request: Request, chat_request: ChatRequest): if not chat_request.prompt: raise HTTPException(status_code=400, detail="Prompt cannot be empty") logger.info(f"Received prompt: {chat_request.prompt}, src_lang: {chat_request.src_lang}, tgt_lang: {chat_request.tgt_lang}") EUROPEAN_LANGUAGES = {"deu_Latn", "fra_Latn", "nld_Latn", "spa_Latn", "ita_Latn", "por_Latn", "rus_Cyrl", "pol_Latn"} try: if chat_request.src_lang != "eng_Latn" and chat_request.src_lang not in EUROPEAN_LANGUAGES: translated_prompt = await perform_internal_translation( sentences=[chat_request.prompt], src_lang=chat_request.src_lang, tgt_lang="eng_Latn" ) prompt_to_process = translated_prompt[0] logger.info(f"Translated prompt to English: {prompt_to_process}") else: prompt_to_process = chat_request.prompt logger.info("Prompt in English or European language, no translation needed") response = await llm_manager.generate(prompt_to_process, settings.max_tokens) logger.info(f"Generated response: {response}") if chat_request.tgt_lang != "eng_Latn" and chat_request.tgt_lang not in EUROPEAN_LANGUAGES: translated_response = await perform_internal_translation( sentences=[response], src_lang="eng_Latn", tgt_lang=chat_request.tgt_lang ) final_response = translated_response[0] logger.info(f"Translated response to {chat_request.tgt_lang}: {final_response}") else: final_response = response logger.info(f"Response in {chat_request.tgt_lang}, no translation needed") return ChatResponse(response=final_response) except Exception as e: logger.error(f"Error processing request: {str(e)}") raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}") @app.post("/v1/visual_query/") async def visual_query( file: UploadFile = File(...), query: str = Body(...), src_lang: str = Query("kan_Knda", enum=list(SUPPORTED_LANGUAGES)), tgt_lang: str = Query("kan_Knda", enum=list(SUPPORTED_LANGUAGES)), ): try: image = Image.open(file.file) if image.size == (0, 0): raise HTTPException(status_code=400, detail="Uploaded image is empty or invalid") if src_lang != "eng_Latn": translated_query = await perform_internal_translation( sentences=[query], src_lang=src_lang, tgt_lang="eng_Latn" ) query_to_process = translated_query[0] logger.info(f"Translated query to English: {query_to_process}") else: query_to_process = query logger.info("Query already in English, no translation needed") answer = await llm_manager.vision_query(image, query_to_process) logger.info(f"Generated English answer: {answer}") if tgt_lang != "eng_Latn": translated_answer = await perform_internal_translation( sentences=[answer], src_lang="eng_Latn", tgt_lang=tgt_lang ) final_answer = translated_answer[0] logger.info(f"Translated answer to {tgt_lang}: {final_answer}") else: final_answer = answer logger.info("Answer kept in English, no translation needed") return {"answer": final_answer} except Exception as e: logger.error(f"Error processing request: {str(e)}") raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}") @app.post("/v1/chat_v2", response_model=ChatResponse) @limiter.limit(settings.chat_rate_limit) async def chat_v2( request: Request, prompt: str = Form(...), image: UploadFile = File(default=None), src_lang: str = Form("kan_Knda"), tgt_lang: str = Form("kan_Knda"), ): if not prompt: raise HTTPException(status_code=400, detail="Prompt cannot be empty") if src_lang not in SUPPORTED_LANGUAGES or tgt_lang not in SUPPORTED_LANGUAGES: raise HTTPException(status_code=400, detail=f"Unsupported language code. Supported codes: {', '.join(SUPPORTED_LANGUAGES)}") logger.info(f"Received prompt: {prompt}, src_lang: {src_lang}, tgt_lang: {tgt_lang}, Image provided: {image is not None}") try: if image: image_data = await image.read() if not image_data: raise HTTPException(status_code=400, detail="Uploaded image is empty") img = Image.open(io.BytesIO(image_data)) if src_lang != "eng_Latn": translated_prompt = await perform_internal_translation( sentences=[prompt], src_lang=src_lang, tgt_lang="eng_Latn" ) prompt_to_process = translated_prompt[0] logger.info(f"Translated prompt to English: {prompt_to_process}") else: prompt_to_process = prompt logger.info("Prompt already in English, no translation needed") decoded = await llm_manager.chat_v2(img, prompt_to_process) logger.info(f"Generated English response: {decoded}") if tgt_lang != "eng_Latn": translated_response = await perform_internal_translation( sentences=[decoded], src_lang="eng_Latn", tgt_lang=tgt_lang ) final_response = translated_response[0] logger.info(f"Translated response to {tgt_lang}: {final_response}") else: final_response = decoded logger.info("Response kept in English, no translation needed") else: if src_lang != "eng_Latn": translated_prompt = await perform_internal_translation( sentences=[prompt], src_lang=src_lang, tgt_lang="eng_Latn" ) prompt_to_process = translated_prompt[0] logger.info(f"Translated prompt to English: {prompt_to_process}") else: prompt_to_process = prompt logger.info("Prompt already in English, no translation needed") decoded = await llm_manager.generate(prompt_to_process, settings.max_tokens) logger.info(f"Generated English response: {decoded}") if tgt_lang != "eng_Latn": translated_response = await perform_internal_translation( sentences=[decoded], src_lang="eng_Latn", tgt_lang=tgt_lang ) final_response = translated_response[0] logger.info(f"Translated response to {tgt_lang}: {final_response}") else: final_response = decoded logger.info("Response kept in English, no translation needed") return ChatResponse(response=final_response) except Exception as e: logger.error(f"Error processing request: {str(e)}") raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}") class TranscriptionResponse(BaseModel): text: str class ASRModelManager: def __init__(self, device_type="cuda"): self.device_type = device_type self.model_language = { "kannada": "kn", "hindi": "hi", "malayalam": "ml", "assamese": "as", "bengali": "bn", "bodo": "brx", "dogri": "doi", "gujarati": "gu", "kashmiri": "ks", "konkani": "kok", "maithili": "mai", "manipuri": "mni", "marathi": "mr", "nepali": "ne", "odia": "or", "punjabi": "pa", "sanskrit": "sa", "santali": "sat", "sindhi": "sd", "tamil": "ta", "telugu": "te", "urdu": "ur" } from fastapi import FastAPI, UploadFile import torch import torchaudio from transformers import AutoModel import argparse import uvicorn from pydantic import BaseModel from pydub import AudioSegment from fastapi import FastAPI, File, UploadFile, HTTPException, Query from fastapi.responses import RedirectResponse, JSONResponse from typing import List # Load the model model = AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True) asr_manager = ASRModelManager() # Language to script mapping LANGUAGE_TO_SCRIPT = { "kannada": "kan_Knda", "hindi": "hin_Deva", "malayalam": "mal_Mlym", "tamil": "tam_Taml", "telugu": "tel_Telu", "assamese": "asm_Beng", "bengali": "ben_Beng", "gujarati": "guj_Gujr", "marathi": "mar_Deva", "odia": "ory_Orya", "punjabi": "pan_Guru", "urdu": "urd_Arab", # Add more as needed } @app.post("/transcribe/", response_model=TranscriptionResponse) async def transcribe_audio(file: UploadFile = File(...), language: str = Query(..., enum=list(asr_manager.model_language.keys()))): try: wav, sr = torchaudio.load(file.file) wav = torch.mean(wav, dim=0, keepdim=True) target_sample_rate = 16000 if sr != target_sample_rate: resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sample_rate) wav = resampler(wav) transcription_rnnt = model(wav, asr_manager.model_language[language], "rnnt") return TranscriptionResponse(text=transcription_rnnt) except Exception as e: logger.error(f"Error in transcription: {str(e)}") raise HTTPException(status_code=500, detail=f"Transcription failed: {str(e)}") @app.post("/v1/speech_to_speech") async def speech_to_speech( request: Request, # Inject Request object from FastAPI file: UploadFile = File(...), language: str = Query(..., enum=list(asr_manager.model_language.keys())), voice: str = Body(default=config.voice) ) -> StreamingResponse: # Step 1: Transcribe audio to text transcription = await transcribe_audio(file, language) logger.info(f"Transcribed text: {transcription.text}") # Step 2: Process text with chat endpoint chat_request = ChatRequest( prompt=transcription.text, src_lang=LANGUAGE_TO_SCRIPT.get(language, "kan_Knda"), # Dynamic script mapping tgt_lang=LANGUAGE_TO_SCRIPT.get(language, "kan_Knda") ) processed_text = await chat(request, chat_request) # Pass the injected request logger.info(f"Processed text: {processed_text.response}") # Step 3: Convert processed text to speech audio_response = await generate_audio( input=processed_text.response, voice=voice, model=tts_config.model, response_format=config.response_format, speed=SPEED ) return audio_response class BatchTranscriptionResponse(BaseModel): transcriptions: List[str] import json if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run the FastAPI server.") parser.add_argument("--port", type=int, default=settings.port, help="Port to run the server on.") parser.add_argument("--host", type=str, default=settings.host, help="Host to run the server on.") parser.add_argument("--config", type=str, default="config_one", help="Configuration to use (e.g., config_one, config_two, config_three, config_four)") args = parser.parse_args() # Load the JSON configuration file def load_config(config_path="dhwani_config.json"): with open(config_path, "r") as f: return json.load(f) config_data = load_config() if args.config not in config_data["configs"]: raise ValueError(f"Invalid config: {args.config}. Available: {list(config_data['configs'].keys())}") selected_config = config_data["configs"][args.config] global_settings = config_data["global_settings"] # Update settings based on selected config settings.llm_model_name = selected_config["components"]["LLM"]["model"] settings.max_tokens = selected_config["components"]["LLM"]["max_tokens"] settings.host = global_settings["host"] settings.port = global_settings["port"] settings.chat_rate_limit = global_settings["chat_rate_limit"] settings.speech_rate_limit = global_settings["speech_rate_limit"] # Initialize LLMManager with the selected LLM model llm_manager = LLMManager(settings.llm_model_name) # Initialize ASR model if present in config if selected_config["components"]["ASR"]: asr_model_name = selected_config["components"]["ASR"]["model"] model = AutoModel.from_pretrained(asr_model_name, trust_remote_code=True) asr_manager.model_language[selected_config["language"]] = selected_config["components"]["ASR"]["language_code"] # Initialize TTS model if present in config if selected_config["components"]["TTS"]: tts_model_name = selected_config["components"]["TTS"]["model"] tts_config.model = tts_model_name # Update tts_config to use the selected model tts_model_manager.get_or_load_model(tts_model_name) # Initialize Translation models - load all specified models if selected_config["components"]["Translation"]: for translation_config in selected_config["components"]["Translation"]: src_lang = translation_config["src_lang"] tgt_lang = translation_config["tgt_lang"] model_manager.get_model(src_lang, tgt_lang) # Override host and port from command line arguments if provided host = args.host if args.host != settings.host else settings.host port = args.port if args.port != settings.port else settings.port # Run the server uvicorn.run(app, host=host, port=port)