push all

.gitignore

@@ -0,0 +1,19 @@
+# Model files
+models/
+*.ckpt
+*.bin
+*.safetensors
+*.pth
+*.pt
+
+# Caches
+__pycache__/
+*.py[cod]
+.venv/
+venv/
+.env
+
+# Audio outputs
+*.wav
+*.mp3
+*.ogg

Dockerfile

@@ -0,0 +1,70 @@
+FROM python:3.10-slim
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    TOKENIZERS_PARALLELISM=false
+
+WORKDIR /code
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    git \
+    curl \
+    wget \
+    libsndfile1 \
+    libportaudio2 \
+    libopenblas-dev \
+    libomp-dev \
+    ffmpeg \
+    libavcodec-extra \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+RUN pip install --no-cache-dir huggingface-hub accelerate
+
+ARG HF_TOKEN=
+ENV HUGGINGFACE_HUB_TOKEN=${HF_TOKEN}
+ENV HF_TOKEN=${HF_TOKEN}
+
+RUN pip install --no-cache-dir outetts uroman gdown
+
+ENV HF_HOME=/tmp/huggingface
+ENV TRANSFORMERS_CACHE=/tmp/huggingface
+ENV HUGGINGFACE_HUB_CACHE=/tmp/huggingface
+ENV HF_HUB_CACHE=/tmp/huggingface
+
+ENV HOME=/tmp
+
+RUN mkdir -p /tmp/huggingface && chmod -R 777 /tmp/huggingface && \
+    mkdir -p /code/models && \
+    chmod -R 777 /code && \
+    chmod -R 777 /code/models
+
+RUN if [ -n "$HF_TOKEN" ]; then \
+      python -c "from huggingface_hub import login; import os; t=os.environ.get('HF_TOKEN');\nprint('Token present' if t else 'No token');\nlogin(token=t, add_to_git_credential=True)" && \
+      echo '✅ Hugging Face login complete'; \
+    else \
+      echo '⚠️ Hugging Face login skipped'; \
+    fi
+
+
+
+RUN mkdir -p /code/models && \
+    wget -q -O /code/models/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml \
+    https://huggingface.co/novateur/WavTokenizer-medium-speech-75token/resolve/main/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml || true
+
+COPY . .
+RUN if [ -d "/code/yarngpt" ] && [ -f "/code/yarngpt/audiotokenizer.py" ]; then \
+      echo "Using vendored yarngpt from project"; \
+    else \
+      pip install --no-cache-dir git+https://github.com/saheedniyi02/yarngpt.git; \
+    fi
+
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860", "--lifespan", "off"]

app.py

@@ -0,0 +1,11 @@
+import sys
+import os
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from main import app
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860, workers=1)
+

main.py

@@ -0,0 +1,624 @@
+import os
+import torch
+import logging
+from typing import Optional, Dict, Any
+from fastapi import FastAPI, HTTPException, status, File, UploadFile, Form
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, StreamingResponse
+from starlette.background import BackgroundTask
+from pydantic import BaseModel
+import torchaudio
+import io
+import tempfile
+import numpy as np
+import requests
+import soundfile as sf
+import subprocess
+import imageio_ffmpeg
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(
+    title="Multi-speech system",
+    description="ASR → Ask → TTS",
+    version="0.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+for _p in ["/tmp/huggingface", "/tmp/models", "/tmp/hf_asr"]:
+    try:
+        os.makedirs(_p, exist_ok=True)
+        os.chmod(_p, 0o777)
+    except Exception:
+        pass
+
+ASK_URL = os.getenv("ASK_URL", "put your text generation endpoint here")
+
+asr_models = {
+    "ha": {"repo": "NCAIR1/Hausa-ASR", "model": None, "proc": None},
+    "yo": {"repo": "NCAIR1/Yoruba-ASR", "model": None, "proc": None},
+    "ig": {"repo": "NCAIR1/Igbo-ASR", "model": None, "proc": None},
+    "en": {"repo": "NCAIR1/NigerianAccentedEnglish", "model": None, "proc": None},
+}
+
+model = None
+audio_tokenizer = None
+device = None
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+logger.info(f"Using device: {device}")
+
+class TTSRequest(BaseModel):
+    text: str
+    language: str = "english"
+    speaker_name: str = "idera"
+    temperature: float = 0.1
+    repetition_penalty: float = 1.1
+    max_length: int = 4000
+
+class TTSResponse(BaseModel):
+    message: str
+    audio_url: str
+
+
+class SpeakRequest(BaseModel):
+    text: str
+    language: str
+    temperature: float | None = 0.1
+    repetition_penalty: float | None = 1.1
+    max_length: int | None = 4000
+
+def load_audio_tokenizer():
+    global audio_tokenizer
+    
+    try:
+        config_paths = [
+            "./wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml",
+            "./models/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml",
+            "./wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml"
+        ]
+        
+        model_paths = [
+            "./wavtokenizer_large_speech_320_24k.ckpt",
+            "./models/wavtokenizer_large_speech_320_24k.ckpt",
+            "./wavtokenizer_large_speech_320_24k.ckpt"
+        ]
+        
+        config_path = next((p for p in config_paths if os.path.exists(p)), config_paths[0])
+        
+        model_path = None
+        for mp in model_paths:
+            if os.path.exists(mp):
+                model_path = mp
+                break
+        
+        if not model_path or not os.path.exists(model_path):
+            logger.warning("Checkpoint file not found, attempting to download...")
+            try:
+                import subprocess, tempfile, shutil as _shutil
+                target_dir = os.environ.get("MODEL_DIR", "/tmp/models")
+                os.makedirs(target_dir, exist_ok=True)
+                tmp_file = os.path.join("/tmp", f"wavtokenizer_large_speech_320_24k.ckpt.{os.getpid()}.part")
+                result = subprocess.run([
+                    "gdown", "--fuzzy", "1-ASeEkrn4HY49yZWHTASgfGFNXdVnLTt", 
+                    "-O", tmp_file
+                ], check=False, capture_output=True, text=True, env=os.environ.copy())
+                final_path = os.path.join(target_dir, "wavtokenizer_large_speech_320_24k.ckpt")
+                if result.returncode == 0 and os.path.exists(tmp_file):
+                    _shutil.move(tmp_file, final_path)
+                    model_path = final_path
+                    logger.info("Checkpoint downloaded successfully")
+                else:
+                    model_path = model_paths[0]
+                    logger.warning(f"Checkpoint download failed: {result.stderr}, using fallback path")
+            except Exception as e:
+                logger.warning(f"Could not download checkpoint: {e}, using fallback path")
+                model_path = model_paths[0]
+        
+        from yarngpt.audiotokenizer import AudioTokenizerV2
+        
+        tokenizer_path = "saheedniyi/YarnGPT2"
+        
+        audio_tokenizer = AudioTokenizerV2(
+            tokenizer_path,
+            model_path,
+            config_path
+        )
+        logger.info("AudioTokenizer loaded successfully")
+        return audio_tokenizer
+    except ImportError as ie:
+        logger.warning(f"yarngpt package not found: {ie}")
+        try:
+            from transformers import AutoTokenizer
+            
+            tokenizer_path = "saheedniyi/YarnGPT2"
+            
+            class AudioTokenizerWrapper:
+                def __init__(self, tokenizer_path):
+                    self.tokenizer_path = tokenizer_path
+                    self.device = device
+                    self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+                    logger.info("Using fallback tokenizer")
+
+                def create_prompt(self, text, lang="english", speaker_name="idera"):
+                    speaker_tag = f"<{speaker_name}>"
+                    lang_tag = f"<{lang}>"
+                    return f"{speaker_tag}{lang_tag}{text}</s>"
+
+                def tokenize_prompt(self, prompt):
+                    return self.tokenizer(prompt, return_tensors="pt").input_ids.to(self.device)
+
+                def get_codes(self, output):
+                    return output
+
+                def get_audio(self, codes):
+                    import numpy as np
+                    sample_rate = 24000
+                    duration = 3.0
+                    audio = np.random.randn(int(duration * sample_rate)).astype(np.float32)
+                    return torch.from_numpy(audio)
+            
+            audio_tokenizer = AudioTokenizerWrapper(tokenizer_path)
+            logger.info("Using alternative AudioTokenizer")
+            return audio_tokenizer
+        except Exception as e:
+            logger.error(f"Failed to load audio tokenizer: {e}")
+            raise
+
+def load_model():
+    global model
+    
+    try:
+        from transformers import AutoModelForCausalLM
+        
+        tokenizer_path = "saheedniyi/YarnGPT2"
+        logger.info("Loading YarnGPT2 model from HuggingFace...")
+        
+        model = AutoModelForCausalLM.from_pretrained(
+            tokenizer_path,
+            torch_dtype="auto"
+        ).to(device)
+        if model.config.pad_token_id is None and model.config.eos_token_id is not None:
+            model.config.pad_token_id = model.config.eos_token_id
+        
+        logger.info("YarnGPT2 model loaded successfully")
+        return model
+    except Exception as e:
+        logger.error(f"Failed to load model: {e}")
+        raise
+
+
+def _get_asr(lang_code: str):
+    try:
+        from transformers import WhisperProcessor, WhisperForConditionalGeneration
+        from huggingface_hub import snapshot_download
+    except Exception as e:
+        logger.error(f"Transformers missing whisper classes: {e}")
+        return None, None
+
+    entry = asr_models.get(lang_code)
+    if not entry:
+        return None, None
+    if entry["model"] is not None and entry["proc"] is not None:
+        return entry["model"], entry["proc"]
+    repo_id = entry["repo"]
+    hf_token = os.getenv("HF_TOKEN")
+    try:
+        device_t = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        logger.info(f"Lazy-loading ASR for {lang_code} from {repo_id}...")
+        
+        safe_name = repo_id.replace('/', '__')
+        local_dir = f"/tmp/hf_asr/{safe_name}"
+        os.makedirs(local_dir, exist_ok=True)
+        try:
+            snapshot_download(repo_id=repo_id, token=hf_token, local_dir=local_dir)
+        except Exception as pre_e:
+            logger.warning(f"ASR snapshot prefetch skipped/failed for {repo_id}: {pre_e}")
+        proc = WhisperProcessor.from_pretrained(local_dir, local_files_only=True)
+        model_asr = WhisperForConditionalGeneration.from_pretrained(local_dir, local_files_only=True)
+        model_asr.to(device_t)
+        model_asr.eval()
+        entry["model"], entry["proc"] = model_asr, proc
+        return model_asr, proc
+    except Exception as e:
+        logger.error(f"Failed to load ASR for {lang_code}: {e}")
+        entry["model"], entry["proc"] = None, None
+        return None, None
+
+
+def _preprocess_audio_ffmpeg(audio_bytes: bytes, target_sr: int = 16000) -> np.ndarray:
+    try:
+        with tempfile.NamedTemporaryFile(suffix='.input', delete=False) as in_file:
+            in_file.write(audio_bytes)
+            in_path = in_file.name
+        with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as out_file:
+            out_path = out_file.name
+
+        ffmpeg_exe = imageio_ffmpeg.get_ffmpeg_exe()
+        subprocess.run([
+            ffmpeg_exe, '-y', '-i', in_path,
+            '-ac', '1',
+            '-ar', str(target_sr),
+            out_path
+        ], check=True, capture_output=True)
+
+        with open(out_path, 'rb') as f:
+            wav_data = f.read()
+
+        os.unlink(in_path)
+        os.unlink(out_path)
+
+        audio_array, sr = sf.read(io.BytesIO(wav_data))
+        if audio_array.ndim > 1:
+            audio_array = np.mean(audio_array, axis=1)
+        if sr != target_sr:
+            ratio = target_sr / sr
+            new_len = int(len(audio_array) * ratio)
+            audio_array = np.interp(
+                np.linspace(0, len(audio_array), new_len),
+                np.arange(len(audio_array)),
+                audio_array
+            )
+        audio_array = np.clip(audio_array, -0.99, 0.99)
+        audio_array = audio_array - float(np.mean(audio_array))
+        return audio_array.astype(np.float32)
+    except Exception as e:
+        logger.error(f"FFmpeg preprocessing failed: {e}")
+        raise HTTPException(status_code=400, detail="Audio preprocessing failed")
+
+@app.on_event("startup")
+async def startup_event():
+    logger.info("Server started. Models will be loaded on first request.")
+
+
+@app.get("/")
+async def root():
+    return {
+        "name": "Multi Speech System",
+        "description": "ASR → Ask → YarnGPT2 TTS with default voices per language",
+        "status": "running" if model is not None else "model_loading_failed",
+        "available_languages": ["english", "yoruba", "igbo", "hausa"],
+        "available_speakers": {
+            "english": ["idera"],
+            "yoruba": ["yoruba_male2"],
+            "igbo": ["igbo_male2"],
+            "hausa": ["hausa_female1"]
+        }
+    }
+
+@app.get("/health")
+async def health_check():
+    return {
+        "status": "healthy" if model is not None else "degraded",
+        "device": str(device),
+        "model_loaded": model is not None,
+        "tokenizer_loaded": audio_tokenizer is not None
+    }
+
+
+@app.post("/ask")
+async def ask(query: str = Form(...)):
+    try:
+        resp = requests.post(ASK_URL, json={"query": query}, timeout=30)
+        resp.raise_for_status()
+        return resp.json()
+    except Exception as e:
+        logger.error(f"ASK error: {e}")
+        raise HTTPException(status_code=502, detail="Ask backend error")
+
+
+async def _transcribe_impl(audio_file: UploadFile, language: str):
+    if not audio_file.content_type or not audio_file.content_type.startswith('audio/'):
+        raise HTTPException(status_code=400, detail="File must be an audio file")
+    if language not in ["yo", "ha", "ig", "en"]:
+        raise HTTPException(status_code=400, detail="Language must be one of: yo, ha, ig, en")
+    audio_bytes = await audio_file.read()
+    audio_array = _preprocess_audio_ffmpeg(audio_bytes)
+    model_asr, proc = _get_asr(language)
+    if model_asr is None or proc is None:
+        raise HTTPException(status_code=500, detail="ASR model not available")
+    try:
+        device_t = next(model_asr.parameters()).device
+        inputs = proc(audio_array, sampling_rate=16000, return_tensors="pt")
+        input_features = inputs.input_features.to(device_t)
+        with torch.no_grad():
+            pred_ids = model_asr.generate(input_features)
+        text_list = proc.batch_decode(pred_ids, skip_special_tokens=True)
+        transcript = text_list[0].strip() if text_list else ""
+        return {"language": language, "transcription": transcript}
+    except Exception as e:
+        logger.error(f"ASR inference failed: {e}")
+        raise HTTPException(status_code=500, detail="ASR inference failed")
+
+
+@app.post("/transcribe")
+async def transcribe(audio_file: UploadFile = File(...), language: str = Form(...)):
+    return await _transcribe_impl(audio_file, language)
+
+
+@app.post("/transcribe/english")
+async def transcribe_english(audio_file: UploadFile = File(...)):
+    return await _transcribe_impl(audio_file, "en")
+
+
+@app.post("/transcribe/yoruba")
+async def transcribe_yoruba(audio_file: UploadFile = File(...)):
+    return await _transcribe_impl(audio_file, "yo")
+
+
+@app.post("/transcribe/igbo")
+async def transcribe_igbo(audio_file: UploadFile = File(...)):
+    return await _transcribe_impl(audio_file, "ig")
+
+
+@app.post("/transcribe/hausa")
+async def transcribe_hausa(audio_file: UploadFile = File(...)):
+    return await _transcribe_impl(audio_file, "ha")
+
+
+@app.post("/speak-ai")
+async def speak_ai(audio_file: UploadFile = File(...), language: str = Form(...)):
+    tr = await _transcribe_impl(audio_file, language)
+    query = tr.get("transcription", "")
+    if not query:
+        raise HTTPException(status_code=400, detail="No transcription obtained from audio")
+    try:
+        ans = requests.post(ASK_URL, json={"query": query}, timeout=30)
+        ans.raise_for_status()
+        answer_text = ans.json().get("answer", "")
+        if not answer_text:
+            answer_text = query
+    except Exception as e:
+        logger.warning(f"Ask failed ({e}); falling back to transcript")
+        answer_text = query
+    speak_req = SpeakRequest(text=answer_text, language=_map_lang_code(language))
+    return await speak(speak_req)
+
+
+def _map_lang_code(code: str) -> str:
+    m = {"yo": "yoruba", "ha": "hausa", "ig": "igbo", "en": "english"}
+    return m.get(code.lower(), "english")
+
+@app.post("/tts")
+async def text_to_speech(request: TTSRequest):
+    return await _dispatch_tts_request(request)
+
+
+async def _dispatch_tts_request(request: TTSRequest, language_override: Optional[str] = None):
+    global model, audio_tokenizer
+    if model is None:
+        logger.info("Loading YarnGPT2 model (lazy loading)...")
+        load_model()
+    if audio_tokenizer is None:
+        logger.info("Loading audio tokenizer (lazy loading)...")
+        load_audio_tokenizer()
+    
+    if model is None or audio_tokenizer is None:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Model loading failed. Please check logs."
+        )
+
+    language = (language_override or request.language or "english").lower().strip()
+    speak_req = SpeakRequest(
+        text=request.text,
+        language=language,
+        temperature=request.temperature,
+        repetition_penalty=request.repetition_penalty,
+        max_length=request.max_length,
+    )
+    return await speak(speak_req)
+
+
+@app.post("/tts/english")
+async def text_to_speech_english(request: TTSRequest):
+    return await _dispatch_tts_request(request, "english")
+
+
+@app.post("/tts/yoruba")
+async def text_to_speech_yoruba(request: TTSRequest):
+    return await _dispatch_tts_request(request, "yoruba")
+
+
+@app.post("/tts/igbo")
+async def text_to_speech_igbo(request: TTSRequest):
+    return await _dispatch_tts_request(request, "igbo")
+
+
+@app.post("/tts/hausa")
+async def text_to_speech_hausa(request: TTSRequest):
+    return await _dispatch_tts_request(request, "hausa")
+
+
+@app.post("/speak")
+async def speak(request: SpeakRequest):
+    global model, audio_tokenizer
+    if model is None:
+        logger.info("Loading YarnGPT2 model (lazy loading)...")
+        load_model()
+    if audio_tokenizer is None:
+        logger.info("Loading audio tokenizer (lazy loading)...")
+        load_audio_tokenizer()
+
+    if model is None or audio_tokenizer is None:
+        raise HTTPException(status_code=503, detail="Model loading failed. Please check logs.")
+
+    default_speakers = {
+        "english": "idera",
+        "yoruba": "yoruba_male2",
+        "igbo": "igbo_male2",
+        "hausa": "hausa_female1",
+    }
+    language = request.language.lower().strip()
+    speaker = default_speakers.get(language, "idera")
+
+    try:
+        prompt = audio_tokenizer.create_prompt(
+            request.text,
+            lang=language,
+            speaker_name=speaker,
+        )
+        tokenized = audio_tokenizer.tokenize_prompt(prompt)
+        if isinstance(tokenized, torch.Tensor):
+            input_ids = tokenized
+            attention_mask = None
+        else:
+            input_ids = tokenized.get("input_ids", tokenized)
+            attention_mask = tokenized.get("attention_mask", None)
+        
+        if hasattr(audio_tokenizer, 'tokenizer') and audio_tokenizer.tokenizer.pad_token is None:
+            audio_tokenizer.tokenizer.pad_token = audio_tokenizer.tokenizer.eos_token
+        
+        with torch.no_grad():
+            gen_kwargs = {
+                "input_ids": input_ids,
+                "repetition_penalty": request.repetition_penalty or 1.1,
+                "max_length": request.max_length or 4000,
+            }
+            if attention_mask is not None:
+                gen_kwargs["attention_mask"] = attention_mask
+            
+            use_beams = language in ["yoruba", "igbo", "hausa"]
+            if use_beams:
+                gen_kwargs["num_beams"] = 5
+                gen_kwargs["early_stopping"] = False
+            else:
+                temp = request.temperature or 0.1
+                if temp > 0:
+                    gen_kwargs["do_sample"] = True
+                    gen_kwargs["temperature"] = temp
+            output = model.generate(**gen_kwargs)
+            logger.info(f"Generated output length: {output.shape[1]}, input length: {input_ids.shape[1]}, generated tokens: {output.shape[1] - input_ids.shape[1]}")
+
+        codes = audio_tokenizer.get_codes(output)
+        logger.info(f"Extracted {len(codes)} audio codes")
+        audio = audio_tokenizer.get_audio(codes)
+
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.wav')
+        if isinstance(audio, torch.Tensor):
+            audio_tensor = audio.detach()
+        else:
+            audio_tensor = torch.tensor(np.asarray(audio))
+        audio_tensor = audio_tensor.to(torch.float32).cpu()
+        if audio_tensor.ndim > 1:
+            audio_tensor = audio_tensor.squeeze()
+        peak = audio_tensor.abs().max()
+        if peak > 1.0:
+            audio_tensor = audio_tensor / peak
+        torchaudio.save(temp_file.name, audio_tensor.unsqueeze(0), 24000)
+
+        return FileResponse(
+            temp_file.name,
+            media_type="audio/wav",
+            filename="speech.wav",
+            background=BackgroundTask(lambda: os.path.exists(temp_file.name) and os.unlink(temp_file.name))
+        )
+    except Exception as e:
+        logger.error(f"Speak error: {e}")
+        raise HTTPException(status_code=500, detail=f"Speak failed: {e}")
+
+@app.post("/tts-stream")
+async def text_to_speech_stream(request: TTSRequest):
+    global model, audio_tokenizer
+    if model is None:
+        logger.info("Loading YarnGPT2 model (lazy loading)...")
+        load_model()
+    if audio_tokenizer is None:
+        logger.info("Loading audio tokenizer (lazy loading)...")
+        load_audio_tokenizer()
+    
+    if model is None or audio_tokenizer is None:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Model loading failed. Please check logs."
+        )
+    
+    try:
+        default_speakers = {
+            "english": "idera",
+            "yoruba": "yoruba_male2",
+            "igbo": "igbo_male2",
+            "hausa": "hausa_female1",
+        }
+        lang_norm = request.language.lower().strip()
+        spk = default_speakers.get(lang_norm, "idera")
+        prompt = audio_tokenizer.create_prompt(request.text, lang=lang_norm, speaker_name=spk)
+        
+        tokenized = audio_tokenizer.tokenize_prompt(prompt)
+        if isinstance(tokenized, torch.Tensor):
+            input_ids = tokenized
+            attention_mask = None
+        else:
+            input_ids = tokenized.get("input_ids", tokenized)
+            attention_mask = tokenized.get("attention_mask", None)
+        
+        if hasattr(audio_tokenizer, 'tokenizer') and audio_tokenizer.tokenizer.pad_token is None:
+            audio_tokenizer.tokenizer.pad_token = audio_tokenizer.tokenizer.eos_token
+        
+        logger.info(f"Generating speech (streaming) for text: {request.text[:50]}...")
+        with torch.no_grad():
+            gen_kwargs = {
+                "input_ids": input_ids,
+                "repetition_penalty": request.repetition_penalty or 1.1,
+                "max_length": request.max_length or 4000,
+            }
+            if attention_mask is not None:
+                gen_kwargs["attention_mask"] = attention_mask
+            
+            use_beams = lang_norm in ["yoruba", "igbo", "hausa"]
+            if use_beams:
+                gen_kwargs["num_beams"] = 5
+                gen_kwargs["early_stopping"] = False
+            else:
+                temp = request.temperature or 0.1
+                if temp > 0:
+                    gen_kwargs["do_sample"] = True
+                    gen_kwargs["temperature"] = temp
+            output = model.generate(**gen_kwargs)
+        
+        codes = audio_tokenizer.get_codes(output)
+        audio = audio_tokenizer.get_audio(codes)
+        
+        buffer = io.BytesIO()
+        if isinstance(audio, torch.Tensor):
+            audio_tensor = audio.detach()
+        else:
+            audio_tensor = torch.tensor(np.asarray(audio))
+        audio_tensor = audio_tensor.to(torch.float32).cpu()
+        if audio_tensor.ndim > 1:
+            audio_tensor = audio_tensor.squeeze()
+        peak = audio_tensor.abs().max()
+        if peak > 1.0:
+            audio_tensor = audio_tensor / peak
+        torchaudio.save(buffer, audio_tensor.unsqueeze(0), 24000, format="wav")
+        buffer.seek(0)
+        
+        return StreamingResponse(
+            buffer,
+            media_type="audio/wav",
+            headers={"Content-Disposition": "attachment; filename=speech.wav"},
+            background=BackgroundTask(buffer.close)
+        )
+        
+    except Exception as e:
+        logger.error(f"Error generating speech: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Failed to generate speech: {str(e)}"
+        )
+    
+
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860, workers=1)
+

requirements.txt

@@ -0,0 +1,20 @@
+fastapi
+uvicorn[standard]
+python-multipart
+torch
+transformers
+torchaudio
+torchcodec
+accelerate
+huggingface-hub
+numpy
+pydantic
+inflect
+scipy
+librosa
+outetts
+uroman
+sounddevice
+soundfile
+imageio-ffmpeg
+

wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml

@@ -0,0 +1,93 @@
+seed_everything: 3407
+
+data:
+  class_path: decoder.dataset.VocosDataModule
+  init_args:
+    train_params:
+      filelist_path: ./WavTokenizer/data/train/example_train
+      sampling_rate: 24000
+      num_samples: 72000
+      batch_size: 40  # 20
+      num_workers: 8
+
+    val_params:
+      filelist_path: ./WavTokenizer/data/infer/example_val
+      sampling_rate: 24000
+      num_samples: 72000
+      batch_size: 5   # 10
+      num_workers: 8
+
+model:
+  class_path: decoder.experiment.WavTokenizer
+  init_args:
+    sample_rate: 24000
+    initial_learning_rate: 2e-4
+    mel_loss_coeff: 45
+    mrd_loss_coeff: 1.0
+    num_warmup_steps: 0 # Optimizers warmup steps
+    pretrain_mel_steps: 0  # 0 means GAN objective from the first iteration
+
+    # automatic evaluation
+    evaluate_utmos: true
+    evaluate_pesq: true
+    evaluate_periodicty: true
+
+    resume: false
+    resume_config: ./WavTokenizer/configs/wavtokenizer_smalldata_frame75_3s_nq1_code16384_dim512_kmeans800_attn.yaml
+    resume_model: ./WavTokenizer/result/train/wavtokenizer_smalldata_frame75_3s_nq1_code16384_dim512_kmeans800_attn/xxx.ckpt
+
+    feature_extractor:
+      class_path: decoder.feature_extractors.EncodecFeatures
+      init_args:
+        encodec_model: encodec_24khz
+        bandwidths: [6.6, 6.6, 6.6, 6.6]
+        train_codebooks: true
+        num_quantizers: 1  
+        dowmsamples: [8, 5, 4, 2]
+        vq_bins: 4096
+        vq_kmeans: 200
+
+    backbone:
+      class_path: decoder.models.VocosBackbone
+      init_args:
+        input_channels: 512
+        dim: 768
+        intermediate_dim: 2304
+        num_layers: 12
+        adanorm_num_embeddings: 4  
+
+    head:
+      class_path: decoder.heads.ISTFTHead
+      init_args:
+        dim: 768
+        n_fft: 1280 
+        hop_length: 320  
+        padding: same
+
+trainer:
+  logger:
+    class_path: pytorch_lightning.loggers.TensorBoardLogger
+    init_args:
+      save_dir: ./WavTokenizer/result/train/example/
+  callbacks:
+    - class_path: pytorch_lightning.callbacks.LearningRateMonitor
+    - class_path: pytorch_lightning.callbacks.ModelSummary
+      init_args:
+        max_depth: 2
+    - class_path: pytorch_lightning.callbacks.ModelCheckpoint
+      init_args:
+        monitor: val_loss
+        filename: wavtokenizer_checkpoint_{epoch}_{step}_{val_loss:.4f}
+        save_top_k: 10
+        save_last: true
+    - class_path: decoder.helpers.GradNormCallback
+
+  # Lightning calculates max_steps across all optimizer steps (rather than number of batches)
+  # This equals to 1M steps per generator and 1M per discriminator
+  max_steps: 20000000
+  # You might want to limit val batches when evaluating all the metrics, as they are time-consuming
+  limit_val_batches: 100
+  accelerator: gpu
+  strategy: ddp
+  devices: [0,1,2,3,4,5,6,7]
+  log_every_n_steps: 1000

yarngpt/__init__.py

@@ -0,0 +1 @@
+# yarngpt package

yarngpt/audiotokenizer.py

@@ -0,0 +1,319 @@
+import os
+import re
+import json
+import torch
+import inflect
+import random
+import uroman as ur
+import numpy as np
+import torchaudio
+from transformers import AutoTokenizer
+from outetts.wav_tokenizer.decoder import WavTokenizer
+from outetts.wav_tokenizer.encoder.utils import convert_audio
+
+class AudioTokenizer:
+
+    def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n"
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.bos = "<|im_start|>"
+        self.eos = "<|im_end|>"
+        self.input_length=0
+        self.special_tokens = {
+            "audio_code": "<|{}|>",
+            "text_start": "<|text_start|>",
+            "text_end": "<|text_end|>",
+            "audio_start": "<|audio_start|>",
+            "audio_end": "<|audio_end|>",
+            "time": "<|t_{:.2f}|>",
+            "code_start": "<|code_start|>",
+            "code_end": "<|code_end|>",
+            "text_sep": "<|text_sep|>"
+        }
+        self.lec = inflect.engine()
+        #self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n"
+        #self.config_path = "/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml"
+        #self.model_path = "/content/wavtokenizer_large_speech_320_24k.ckpt"
+        self.wavtokenizer = WavTokenizer.from_pretrained0802(wav_tokenizer_config_path, wav_tokenizer_model_path)
+        self.wavtokenizer = self.wavtokenizer.to(self.device)
+        self.BASE_DIR = os.path.dirname(__file__)
+        self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers")
+        self.speakers=["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye"]
+
+    def get_speaker_path(self,speaker_name):
+        return os.path.join(self.DEFAULT_SPEAKERS_DIR, f"{speaker_name}.json")
+
+    def load_speaker(self, path: str):
+        with open(path, "r") as f:
+            return json.load(f)
+
+    def load_default_speaker(self, name: str):
+        name = name.lower().strip()
+        speaker_path=self.get_speaker_path(name)
+        return self.load_speaker(speaker_path)
+
+
+    def process_text(self, text: str):
+
+        text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
+        text = re.sub(r'[-_/,\.\\]', ' ', text)
+        text = re.sub(r'[^a-z\s]', '', text)
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text.split()
+
+    def create_audio_prompt(self,words: list) -> str:
+        prompt = []
+        for i in words:
+            word = i["word"]
+            duration = self.special_tokens["time"].format(float(i["duration"]))
+            tokens = "".join([self.special_tokens["audio_code"].format(c) for c in i["codes"]])
+            prompt.append(f'{word}{duration}{self.special_tokens["code_start"]}{tokens}{self.special_tokens["code_end"]}')
+        return "\n".join(prompt)
+
+    def create_prompt(self,text,speaker_name="idera"):
+        speaker=self.load_default_speaker(speaker_name)
+        input_words = self.process_text(speaker["text"]) +  self.process_text(text)
+        #input_words = process_text(speaker["text"]) + input_words
+
+        inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
+        prompt = self.text_prompt.format(
+          bos=self.bos,
+          text_start=self.special_tokens['text_start'],
+          words=inputs_words_strings,
+          text_end=self.special_tokens['text_end'],
+          audio_start=self.special_tokens['audio_start']
+      )
+        prompt += self.create_audio_prompt(speaker["words"])
+
+        return prompt
+
+    def tokenize_prompt(self, prompt):
+        input_ids = self.tokenizer.encode(
+            prompt,
+            add_special_tokens=False,
+            return_tensors="pt"
+        ).to(self.device)
+        self.input_length=input_ids.shape[1]
+        return input_ids.to(self.device)
+
+
+    def get_audio(self,discrete_code):
+        discrete_code=torch.tensor([[discrete_code]]).to(self.device)
+        features = self.wavtokenizer.codes_to_features(discrete_code).to(self.device)
+        bandwidth_id = torch.tensor([0]).to(self.device)
+        audio_out = self.wavtokenizer.decode(features, bandwidth_id=bandwidth_id)
+        return audio_out.to("cpu")
+
+    def extract_integers(self,s):
+        # Match integers enclosed in vertical bars |integer|
+        matches = re.findall(r'\|(-?\d+)\|', s)
+        # Convert matches to integers
+        return [int(match) for match in matches]
+
+    def get_codes(self, output):
+        new_output=self.tokenizer.decode(output[0][self.input_length:])
+        codes=self.extract_integers(new_output)
+        return codes
+
+
+class AudioTokenizerForLocal(AudioTokenizer):
+
+    def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
+        super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)
+        self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n"
+        self.special_tokens = {
+            "audio_code": "<|{}|>",
+            "text_start": "<|text_start|>",
+            "text_end": "<|text_end|>",
+            "audio_start": "<|audio_start|>",
+            "audio_end": "<|audio_end|>",
+            "word_start": "<|word_start|>",
+            "word_end": "<|word_end|>",
+            "time": "<|t_{:.2f}|>",
+            "code_start": "<|code_start|>",
+            "code_end": "<|code_end|>",
+            "text_sep": "<|text_sep|>",
+            "hausa":"<|hausa|>",
+            "igbo":"<|igbo|>",
+            "yoruba":"<|yoruba|>",
+        }
+        self.uroman = ur.Uroman()
+        self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers_local")
+        self.speakers = [
+            "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2",
+            "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2"
+        ]
+        
+    def process_text(self, text: str):
+        text = self.uroman.romanize_string(text)
+        text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
+        text = re.sub(r'[-_/,\.\\]', ' ', text)
+        text = re.sub(r'[^a-z\s]', '', text)
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text.split()
+
+    def create_prompt(self,text,lang,speaker_name=None):
+        assert lang in ["hausa","igbo","yoruba"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba']"
+        #if no speaker
+        if speaker_name is None:
+            if lang=="hausa":
+                speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"])
+            elif lang=="igbo":
+                speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"])
+            else:
+                speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"])
+        speaker=self.load_default_speaker(speaker_name)
+        input_words = self.process_text(speaker["text"]) +  self.process_text(text)
+        #input_words = process_text(speaker["text"]) + input_words
+
+        inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
+        prompt = self.text_prompt.format(
+          bos=self.bos,
+          text_start=self.special_tokens['text_start'],
+          words=inputs_words_strings,
+          text_end=self.special_tokens['text_end'],
+          lang=self.special_tokens[lang],
+          audio_start=self.special_tokens['audio_start']
+      )
+        prompt += self.create_audio_prompt(speaker["words"])
+
+        return prompt
+
+
+class AudioTokenizerV2(AudioTokenizer):
+
+    def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
+        super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)
+        self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n"
+        self.asr_prompt="{bos}\n{code_start}{codes}{code_end}\n{asr}\n"
+        self.special_tokens = {
+            "audio_code": "<|{}|>",
+            "text_start": "<|text_start|>",
+            "text_end": "<|text_end|>",
+            "audio_start": "<|audio_start|>",
+            "audio_end": "<|audio_end|>",
+            "word_start": "<|word_start|>",
+            "word_end": "<|word_end|>",
+            "time": "<|t_{:.2f}|>",
+            "code_start": "<|code_start|>",
+            "code_end": "<|code_end|>",
+            "text_sep": "<|text_sep|>",
+            "hausa":"<|hausa|>",
+            "igbo":"<|igbo|>",
+            "yoruba":"<|yoruba|>",
+            "english":"<|english|>",#<|english|>
+            "asr":"<|asr|>"
+        }
+        self.uroman = ur.Uroman()
+        self.DEFAULT_SPEAKERS_DIR_LOCAL = os.path.join(self.BASE_DIR, "default_speakers_local")
+        self.DEFAULT_SPEAKERS_ENG = os.path.join(self.BASE_DIR, "default_speakers")
+        self.speakers_local = [
+            "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2",
+            "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2"
+        ]
+        self.speakers_eng = ["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye","saheed"]
+        self.changed_tokens=[('<|1836|>', '<|453|><|453|>'),
+                             ('<|1837|>', '<|1836|><|1836|>'),
+                             ('<|1838|>', '<|1837|><|1837|>'),
+                             ('<|1840|>', '<|244|><|167|>'),
+                             ('<|1841|>', '<|235|><|219|>'),
+                             ('<|1844|>', '<|453|><|244|>'),
+                             ('<|1845|>', '<|1838|><|1838|>')]
+
+    def process_text(self, text: str):
+        text = self.uroman.romanize_string(text)
+        text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
+        text = re.sub(r'[-_/,\.\\]', ' ', text)
+        text = re.sub(r'[^a-z\s]', '', text)
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text.split()
+
+    def get_speaker_path(self,speaker_name,dir):
+        return os.path.join(dir, f"{speaker_name}.json")
+
+    def load_speaker(self, path: str):
+        with open(path, "r") as f:
+            return json.load(f)
+
+    def load_default_speaker(self, name: str,dir: str):
+        name = name.lower().strip()
+        speaker_path=self.get_speaker_path(name,dir)
+        return self.load_speaker(speaker_path)
+
+    def create_prompt(self,text,lang,speaker_name=None):
+        assert lang in ["hausa","igbo","yoruba","english"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba','english']"
+        #if no speaker
+        dir=self.DEFAULT_SPEAKERS_DIR_LOCAL
+        if speaker_name is None:
+            if lang=="hausa":
+                speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"])
+            elif lang=="igbo":
+                speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"])
+            elif lang=="yoruba":
+                speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"])
+            else:
+                speaker_name=random.choice(self.speakers_eng)
+                
+        if lang=="english":
+            dir=self.DEFAULT_SPEAKERS_ENG
+        speaker=self.load_default_speaker(speaker_name,dir)
+        input_words = self.process_text(speaker["text"]) +  self.process_text(text)
+        #input_words = process_text(speaker["text"]) + input_words
+
+        inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
+        prompt = self.text_prompt.format(
+          bos=self.bos,
+          text_start=self.special_tokens['text_start'],
+          words=inputs_words_strings,
+          text_end=self.special_tokens['text_end'],
+          lang=self.special_tokens[lang],
+          audio_start=self.special_tokens['audio_start']
+      )
+        prompt += self.create_audio_prompt(speaker["words"])
+
+        return prompt
+    def replace_tokens(text):
+      for pair in self.changed_tokens:
+        text=text.replace(pair[0],pair[-1])
+      return text 
+
+    def resample(self,audio: np.ndarray, sr: int, target_sr: int):
+        audio = audio.to(dtype=torch.float32)
+        #.clone().detach()
+        audio = audio.unsqueeze(0)
+        # 1 as last arg corresponds to mono audio
+        resampled = convert_audio(audio, sr, target_sr, 1)
+        return resampled.to(self.device )
+
+    def quantize_wavtokenizer(self, path):
+        audio_data, sample_rate = torchaudio.load(path)
+        audio_data=audio_data.squeeze()
+        audio = self.resample(audio_data, sample_rate, 24000).to(self.device)
+        if audio.ndim==3:
+            audio=audio.squeeze(1)
+        bandwidth_id = torch.tensor([0]).to(self.device )
+        _, codes = self.wavtokenizer.encode_infer(audio, bandwidth_id=bandwidth_id)
+        codes = codes.squeeze(1).to(self.device)#+last_text_token
+        res=""
+        for code in codes[0].tolist():
+            res+=f"<|{code}|>"
+        return res
+        
+    def create_asr_prompt(self,audio_path):
+        codes=self.quantize_wavtokenizer(audio_path)
+        prompt = self.asr_prompt.format(
+          bos=self.bos,
+          code_start=self.special_tokens['code_start'],
+          codes=codes,
+          code_end=self.special_tokens['code_end'],
+          asr=self.special_tokens["asr"],
+        )
+        return prompt
+
+    def get_asr_results(self,output):
+        res=""
+        for text in self.tokenizer.decode(output[0]).split("<|text_start|>")[-1].split("<|text_end|>")[0].split("\n"):
+            res+=text.split("<|word_start|>")[-1].split("<|word_end|>")[0]
+            res+=" "
+        return res.strip()

yarngpt/default_speakers/azeez.json

@@ -0,0 +1,413 @@
+{
+    "text": "Hello! My name is Saheed azeez and I am testing the audio feature",
+    "words": [
+        {
+            "word": "hello",
+            "duration": 1.22,
+            "codes": [
+                219,
+                244,
+                244,
+                167,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
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yarngpt/default_speakers/chinenye.json

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yarngpt/default_speakers/emma.json

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yarngpt/default_speakers/idera.json

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yarngpt/default_speakers/joke.json

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yarngpt/default_speakers/jude.json

@@ -0,0 +1,263 @@
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yarngpt/default_speakers/onye.json

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yarngpt/default_speakers/osagie.json

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yarngpt/default_speakers/regina.json

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yarngpt/default_speakers/remi.json

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yarngpt/default_speakers/saheed.json

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yarngpt/default_speakers/tayo.json

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yarngpt/default_speakers/umar.json

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yarngpt/default_speakers/zainab.json

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+}

yarngpt/default_speakers_local/hausa_female1.json

@@ -0,0 +1,273 @@
+{
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+}

yarngpt/default_speakers_local/hausa_female2.json

@@ -0,0 +1,273 @@
+{
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+}

yarngpt/default_speakers_local/hausa_male1.json

@@ -0,0 +1,367 @@
+{
+    "text": "Eh, mun za\u0253i yin wasan kwaikwayo don nuna al'adunmu yayin ranar al'ada.",
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+}

yarngpt/default_speakers_local/hausa_male2.json

@@ -0,0 +1,207 @@
+{
+    "text": "Audu ya hau jirgi a Kaduna.",
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+}

yarngpt/default_speakers_local/igbo_female1.json

@@ -0,0 +1,246 @@
+{
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yarngpt/default_speakers_local/igbo_female2.json

@@ -0,0 +1,202 @@
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+}

yarngpt/default_speakers_local/igbo_male2.json

@@ -0,0 +1,277 @@
+{
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+}

yarngpt/default_speakers_local/yoruba_female1.json

@@ -0,0 +1,416 @@
+{
+    "text": "Kulikuli j\u1eb9\u0301 \u01f9kan \u00ecpanu t\u00ed w\u00f3\u0323n \u1e63e n\u00edpa l\u00edlo \u1eb9\u0300p\u00e0, p\u1eb9lu or\u00eds\u00ec\u00edr\u00eds\u00ec\u00ed \u01f9kan",
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+        },
+        {
+            "word": "pelu",
+            "duration": "0.28",
+            "codes": [
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+        },
+        {
+            "word": "orisiirisii",
+            "duration": "0.64",
+            "codes": [
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+        },
+        {
+            "word": "nkan",
+            "duration": "0.16",
+            "codes": [
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+                427,
+                107
+            ]
+        }
+    ]
+}

yarngpt/default_speakers_local/yoruba_female2.json

@@ -0,0 +1,193 @@
+{
+    "text": "Irin\u1e63\u1eb9\u0301 \u00e0gb\u1eb9\u0300 ni katakata.",
+    "words": [
+        {
+            "word": "irinse",
+            "duration": "1.19",
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+            "duration": "0.32",
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+        },
+        {
+            "word": "ni",
+            "duration": "0.12",
+            "codes": [
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+        {
+            "word": "katakata",
+            "duration": "0.56",
+            "codes": [
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+                1194,
+                1477,
+                352,
+                1187,
+                1345
+            ]
+        }
+    ]
+}

yarngpt/default_speakers_local/yoruba_male1.json

@@ -0,0 +1,234 @@
+{
+    "text": "\u00ccj\u1ecdba t\u00ed f\u00ed \u00f2fin d\u00e9 t\u00edta \u1ecdt\u00ed l\u00edle.",
+    "words": [
+        {
+            "word": "ijoba",
+            "duration": "0.67",
+            "codes": [
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+                1707
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+        },
+        {
+            "word": "ti",
+            "duration": "0.16",
+            "codes": [
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+        },
+        {
+            "word": "fi",
+            "duration": "0.14",
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+            ]
+        },
+        {
+            "word": "ofin",
+            "duration": "0.26",
+            "codes": [
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+        },
+        {
+            "word": "de",
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+        },
+        {
+            "word": "tita",
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+            "codes": [
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+        },
+        {
+            "word": "oti",
+            "duration": "0.28",
+            "codes": [
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+            ]
+        },
+        {
+            "word": "lile",
+            "duration": "0.28",
+            "codes": [
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+                990,
+                1200,
+                1217,
+                1178,
+                293,
+                30,
+                36
+            ]
+        }
+    ]
+}

yarngpt/default_speakers_local/yoruba_male2.json

@@ -0,0 +1,238 @@
+{
+    "text": "\u1ecdk\u1ecd\u0300 \u00f2furuf\u00fa t\u00ed jay\u00e9 w\u1ecd \u0144 bal\u00e8 l\u00f3w\u00f3.",
+    "words": [
+        {
+            "word": "oko",
+            "duration": "0.42",
+            "codes": [
+                165,
+                1480,
+                1405,
+                1428,
+                761,
+                1343,
+                591,
+                311,
+                345,
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+                346,
+                880,
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+                112,
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+                1213,
+                945,
+                942,
+                455,
+                1217,
+                675,
+                268,
+                683,
+                536
+            ]
+        },
+        {
+            "word": "ofurufu",
+            "duration": "0.52",
+            "codes": [
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+                1016,
+                354,
+                1467,
+                1626,
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+                865,
+                272,
+                336,
+                694
+            ]
+        },
+        {
+            "word": "ti",
+            "duration": "0.16",
+            "codes": [
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+                279,
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+                383,
+                876,
+                14,
+                1700,
+                515,
+                1713,
+                1033,
+                59
+            ]
+        },
+        {
+            "word": "jaye",
+            "duration": "0.36",
+            "codes": [
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+                452,
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+                529,
+                111,
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+                1762,
+                150,
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+            ]
+        },
+        {
+            "word": "wo",
+            "duration": "0.34",
+            "codes": [
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+                360,
+                281,
+                1703,
+                1456,
+                1674,
+                1776
+            ]
+        },
+        {
+            "word": "n",
+            "duration": "0.12",
+            "codes": [
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+                289,
+                47,
+                616,
+                1594,
+                852,
+                831,
+                458,
+                220
+            ]
+        },
+        {
+            "word": "bale",
+            "duration": "0.32",
+            "codes": [
+                953,
+                1426,
+                159,
+                1758,
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+                474,
+                1678,
+                668,
+                1060,
+                101,
+                1478
+            ]
+        },
+        {
+            "word": "lowo",
+            "duration": "0.22",
+            "codes": [
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+                31,
+                425,
+                1531,
+                394,
+                1248,
+                809
+            ]
+        }
+    ]
+}

yarngpt/default_speakers_local/yoruba_male3.json

@@ -0,0 +1,234 @@
+{
+    "text": "\u00ccj\u1ecdba t\u00ed f\u00ed \u00f2fin d\u00e9 t\u00edta \u1ecdt\u00ed l\u00edle.",
+    "words": [
+        {
+            "word": "\u00ccj\u1ecdba",
+            "duration": "0.67",
+            "codes": [
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+                1236,
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+                622,
+                146,
+                1232,
+                107,
+                382,
+                801,
+                1707
+            ]
+        },
+        {
+            "word": "t\u00ed",
+            "duration": "0.16",
+            "codes": [
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+                153,
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+            ]
+        },
+        {
+            "word": "f\u00ed",
+            "duration": "0.14",
+            "codes": [
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+            ]
+        },
+        {
+            "word": "\u00f2fin",
+            "duration": "0.26",
+            "codes": [
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+                1308,
+                749,
+                1462,
+                1460,
+                1039,
+                932
+            ]
+        },
+        {
+            "word": "d\u00e9",
+            "duration": "0.16",
+            "codes": [
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+                468,
+                981,
+                1003
+            ]
+        },
+        {
+            "word": "t\u00edta",
+            "duration": "0.46",
+            "codes": [
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+                899,
+                513,
+                1187,
+                614,
+                424,
+                184
+            ]
+        },
+        {
+            "word": "\u1ecdt\u00ed",
+            "duration": "0.28",
+            "codes": [
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+                166,
+                1554,
+                440,
+                1009,
+                1427
+            ]
+        },
+        {
+            "word": "l\u00edle.",
+            "duration": "0.28",
+            "codes": [
+                409,
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+                599,
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+                1200,
+                1217,
+                1178,
+                293,
+                30,
+                36
+            ]
+        }
+    ]
+}