Upload folder using huggingface_hub

config.json

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+{
+  "architectures": [
+    "INF5Model"
+  ],
+  "auto_map": {
+    "AutoConfig": "model.INF5Config",
+    "AutoModel": "model.INF5Model"
+  },
+  "ckpt_path": "model_last.pt",
+  "model_type": "inf5",
+  "remove_sil": true,
+  "speed": 1.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "vocab_path": "vocab.txt"
+}

model.py

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+import sys
+import os
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(current_dir)
+
+from transformers import PreTrainedModel, PretrainedConfig, AutoConfig
+import torch
+import numpy as np
+from f5_tts.infer.utils_infer import (
+    infer_process,
+    load_model,
+    load_vocoder,
+    preprocess_ref_audio_text,
+)
+from f5_tts.model import DiT
+import soundfile as sf
+import io
+from pydub import AudioSegment, silence
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+class INF5Config(PretrainedConfig):
+    model_type = "inf5"
+
+    def __init__(self, ckpt_repo_id: str = None, vocab_repo_id: str = None,
+                 ckpt_filename: str = None, vocab_filename: str = "vocab.txt",
+                 speed: float = 1.0, remove_sil: bool = True, **kwargs):
+        super().__init__(**kwargs)
+        # If not specified, use the model's own repo for both
+        self.ckpt_repo_id = ckpt_repo_id
+        self.vocab_repo_id = vocab_repo_id
+        self.ckpt_filename = ckpt_filename
+        self.vocab_filename = vocab_filename
+        self.speed = speed
+        self.remove_sil = remove_sil
+
+class INF5Model(PreTrainedModel):
+    config_class = INF5Config
+
+    def __init__(self, config):
+        super().__init__(config)
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        # Load vocoder
+        self.vocoder = torch.compile(
+            load_vocoder(vocoder_name="vocos", is_local=False, device=device)
+        )
+
+        # Determine which repo to use for vocab
+        # Default to the model's own repo if not specified
+        vocab_repo = config.vocab_repo_id or config.name_or_path
+        
+        # Download vocab.txt from HF Hub
+        vocab_path = hf_hub_download(repo_id=vocab_repo, filename=config.vocab_filename)
+
+        # Determine which repo to use for checkpoint
+        ckpt_repo = config.ckpt_repo_id or config.name_or_path
+
+        ckpt_candidates = [
+            "model_last.pt",  # Try this first since it's in your repo
+            "checkpoints/model.safetensors",
+            "model.safetensors",
+            "checkpoints/pytorch_model.bin",
+            "pytorch_model.bin",
+            "checkpoints/model.pt",
+            "model.pt",
+            "checkpoints/checkpoint.pt",
+            "checkpoint.pt"
+        ]
+
+        # If a specific checkpoint filename is provided, use only that
+        if config.ckpt_filename:
+            ckpt_candidates = [config.ckpt_filename]
+
+        ckpt_path = None
+
+        for fname in ckpt_candidates:
+            try:
+                ckpt_path = hf_hub_download(repo_id=ckpt_repo, filename=fname)
+                print(f"Found checkpoint on hub: {fname} -> {ckpt_path}")
+                break
+            except Exception as e:
+                # ignore and try next candidate; but log for debugging
+                # common failures: file not found, LFS not enabled, permission issues
+                # print(f"Attempt to download {fname} failed: {e}")
+                continue
+
+        if ckpt_path is None:
+            raise RuntimeError(
+                "Could not find a checkpoint file on the Hub. "
+                "Tried: " + ", ".join(ckpt_candidates) + ".\n"
+                "If your checkpoint is stored under a different path or name, "
+                "update ckpt_candidates or pass the path via config (e.g. config.ckpt_filename). "
+                "If the file is >5GB, ensure Git LFS is enabled for the repo (hf lfs-enable-largefiles)."
+            )
+
+        # Pass ckpt_path to load_model. Use keyword to avoid mismatch in positional args.
+        self.ema_model = torch.compile(
+            load_model(
+                DiT,
+                dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4),
+                mel_spec_type="vocos",
+                vocab_file=vocab_path,
+                device=device,
+                ckpt_path=ckpt_path
+            )
+        )
+
+        # Optionally: if load_model returns an uninitialized model and you want to load a state dict:
+        # state_dict = load_file(ckpt_path, device=str(device))
+        # self.ema_model.load_state_dict(state_dict, strict=False)
+
+    
+    def forward(self, text: str, ref_audio_path: str, ref_text: str):
+        """
+        Generate speech given a reference audio & text input.
+        
+        Args:
+            text (str): The text to be synthesized.
+            ref_audio_path (str): Path to the reference audio file.
+            ref_text (str): The reference text.
+        Returns:
+            np.array: Generated waveform.
+        """
+
+        if not os.path.exists(ref_audio_path):
+            raise FileNotFoundError(f"Reference audio file {ref_audio_path} not found.")
+
+        # Load reference audio & text
+        ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_path, ref_text)
+
+        
+        self.ema_model.to(self.device)
+        self.vocoder.to(self.device)
+        
+        # Perform inference
+        audio, final_sample_rate, _ = infer_process(
+            ref_audio,
+            ref_text,
+            text,
+            self.ema_model,
+            self.vocoder,
+            mel_spec_type="vocos",
+            speed=self.config.speed,
+            device=self.device,
+        )
+
+        # Convert to pydub format and remove silence if needed
+        buffer = io.BytesIO()
+        sf.write(buffer, audio, samplerate=24000, format="WAV")
+        buffer.seek(0)
+        audio_segment = AudioSegment.from_file(buffer, format="wav")
+
+        if self.config.remove_sil:
+            non_silent_segs = silence.split_on_silence(
+                audio_segment,
+                min_silence_len=1000,
+                silence_thresh=-50,
+                keep_silence=500,
+                seek_step=10,
+            )
+            non_silent_wave = sum(non_silent_segs, AudioSegment.silent(duration=0))
+            audio_segment = non_silent_wave
+
+        # Normalize loudness
+        target_dBFS = -20.0
+        change_in_dBFS = target_dBFS - audio_segment.dBFS
+        audio_segment = audio_segment.apply_gain(change_in_dBFS)
+
+        return np.array(audio_segment.get_array_of_samples())
+
+
+
+if __name__ == '__main__':
+    model = INF5Model(INF5Config())
+    model.save_pretrained("INF5")
+    model.config.save_pretrained("INF5")
+    
+
+    # import numpy as np
+    # import soundfile as sf
+    # from transformers import AutoConfig, AutoModel
+    
+    # AutoConfig.register("inf5", INF5Config)
+    # AutoModel.register(INF5Config, INF5Model)
+
+    # model = AutoModel.from_pretrained("INF5")
+    # audio = model("नमस्ते! संगीत की तरह जीवन भी खूबसूरत होता है, बस इसे सही ताल में जीना आना चाहिए.", 
+    #               ref_audio_path="prompts/PAN_F_HAPPY_00001.wav",
+    #               ref_text="भਹੰਪੀ ਵਿੱਚ ਸਮਾਰਕਾਂ ਦੇ ਭਵਨ ਨਿਰਮਾਣ ਕਲਾ ਦੇ ਵੇਰਵੇ ਗੁੰਝਲਦਾਰ ਅਤੇ ਹੈਰਾਨ ਕਰਨ ਵਾਲੇ ਹਨ, ਜੋ ਮੈਨੂੰ ਖੁਸ਼ ਕਰਦੇ  ਹਨ।")
+    
+    # if audio.dtype == np.int16:
+    #     audio = audio.astype(np.float32) / 32768.0  
+    # sf.write("samples/namaste.wav", np.array(audio, dtype=np.float32), samplerate=24000)
+
+    # from huggingface_hub import HfApi
+
+    # repo_id = "svp19/INF5"  # Change to your HF repo
+
+    # # Upload model directory to HF
+    # api = HfApi()
+    # api.upload_folder(
+    #     folder_path="INF5",
+    #     repo_id=repo_id,
+    #     repo_type="model"
+    # )
+    # print(f"Model pushed to https://huggingface.co/{repo_id} 🚀")
+
+    # print("Verify Upload")
+    # from transformers import AutoModel
+    # model = AutoModel.from_pretrained(repo_id)
+    # print("Success")
+

model_last.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ff25444449384e93e474d9b492657a026bcbb285f0d5939b7bcb070c49533668
+size 5394189187

vocab.txt

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+ 
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+/
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+:
+;
+=
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+항
+해
+햇
+했
+행
+허
+험
+형
+혜
+호
+혼
+홀
+화
+회
+획
+후
+휴
+흐
+흔
+희
+히
+힘
+ﷺ
+ﷻ
+！
+，
+？
+�
+𠮶