Orpheus (tr)

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tr/Orpheus-TTS-TR-Beta/README.md

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+---
+library_name: transformers
+tags:
+- unsloth
+datasets:
+- erenfazlioglu/turkishvoicedataset
+language:
+- tr
+base_model:
+- canopylabs/orpheus-3b-0.1-pretrained
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

tr/Orpheus-TTS-TR-Beta/config.json

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+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.50.1",
+  "unsloth_version": "2025.3.19",
+  "use_cache": true,
+  "vocab_size": 156939
+}

tr/Orpheus-TTS-TR-Beta/generation_config.json

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+  "top_p": 0.9,
+  "transformers_version": "4.50.1"
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tr/Orpheus-TTS-TR-Beta/source.txt

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tr/orpheus-tts-tr-full/README.md

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+---
+library_name: transformers
+tags:
+- unsloth
+datasets:
+- erenfazlioglu/turkishvoicedataset
+language:
+- tr
+base_model:
+- canopylabs/orpheus-3b-0.1-pretrained
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

tr/orpheus-tts-tr-full/config.json

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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
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tr/orpheus-tts-tr-full/source.txt

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+https://huggingface.co/kadirnar/orpheus-tts-tr-full

tr/orpheus-tts-tr-full/special_tokens_map.json

@@ -0,0 +1,20 @@
+{
+  "additional_special_tokens": [
+    "<|audio|>"
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|finetune_right_pad_id|>"
+}

tr/orpheus-tts-tr-full/tokenizer.json

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tr/orpheust-tts-base-fine-tune/README.md

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+---
+base_model: unsloth/orpheus-3b-0.1-ft
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+- tts
+license: apache-2.0
+language:
+- tr
+pipeline_tag: text-to-speech
+---
+
+# Uploaded  model
+
+- **Developed by:** Cosmobillian
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+
+
+model_name = "Cosmobillian/orpheust-tts-base-fine-tune"
+# model_name = "canopylabs/orpheus-3b-0.1-ft"
+# Restart the kernel if needed
+from snac import SNAC
+import torch
+import torch
+from transformers import AutoModelForCausalLM, Trainer, TrainingArguments, AutoTokenizer
+import numpy as np
+import soundfile as sf
+import IPython.display as ipd
+import librosa
+from ipywebrtc import AudioRecorder, Audio
+from IPython.display import display
+import ipywidgets as widgets
+from huggingface_hub import snapshot_download
+import torchaudio.transforms as T
+import librosa
+import torch
+from IPython.display import Audio, display
+
+device = "cuda" if torch.cuda.is_available() else "mps" #or cpu if you aren't on an M type mac
+print(device)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+
+
+
+# Download only model config and safetensors
+
+model_path = snapshot_download(
+    repo_id=model_name,
+    allow_patterns=[
+        "config.json",
+        "*.safetensors",
+        "model.safetensors.index.json",
+    ],
+    ignore_patterns=[
+        "optimizer.pt",
+        "pytorch_model.bin",
+        "training_args.bin",
+        "scheduler.pt",
+        "tokenizer.json",
+        "tokenizer_config.json",
+        "special_tokens_map.json",
+        "vocab.json",
+        "merges.txt",
+        "tokenizer.*"
+    ]
+)
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
+model.to(device)
+
+
+
+### CHANGE THIS TO YOUR OWN FILE AND TEXT
+
+my_wav_file_is = "/content/drive/MyDrive/Colab Notebooks/short15s_sezen_aksu.wav"
+and_the_transcript_is = "Ayşeciğin filmi var zeynep değirmencioğlunun hafızasını kaybediyor yolda birileri buluyorlar "
+
+the_model_should_say = [
+    "Hayat, her gün karşımıza yeni fırsatlar ve zorluklar çıkarır. Önemli olan, bu anları nasıl değerlendirdiğimizdir. Bazen küçük bir adım bile büyük değişimlerin başlangıcı olabilir. Her sabah yeni bir başlangıçtır; dünü geride bırakıp bugünü en iyi şekilde değerlendirmek elimizde. İnsan, hedeflerine ulaşmak için kararlılıkla ilerlemeli ve karşılaştığı engellerden yılmadan yoluna devam etmelidir."
+
+]
+#@title Tokenising your stuff for the prompt
+''' Here we tokenise the prompt you gave us, we also tokenise the prompts you want the model to say
+
+The template is:
+
+start_of_human, start_of_text, text, end_of_text, start_of_ai, start_of_speech, speech, end_of_speech, end_of_ai, start_of_human, text, end_of_human and then generate from here
+
+'''
+
+
+filename = my_wav_file_is
+
+audio_array, sample_rate = librosa.load(filename, sr=24000)
+
+def tokenise_audio(waveform):
+  waveform = torch.from_numpy(waveform).unsqueeze(0)
+  waveform = waveform.to(dtype=torch.float32)
+
+
+  waveform = waveform.unsqueeze(0)
+
+  with torch.inference_mode():
+    codes = snac_model.encode(waveform)
+
+  all_codes = []
+  for i in range(codes[0].shape[1]):
+    all_codes.append(codes[0][0][i].item()+128266)
+    all_codes.append(codes[1][0][2*i].item()+128266+4096)
+    all_codes.append(codes[2][0][4*i].item()+128266+(2*4096))
+    all_codes.append(codes[2][0][(4*i)+1].item()+128266+(3*4096))
+    all_codes.append(codes[1][0][(2*i)+1].item()+128266+(4*4096))
+    all_codes.append(codes[2][0][(4*i)+2].item()+128266+(5*4096))
+    all_codes.append(codes[2][0][(4*i)+3].item()+128266+(6*4096))
+
+
+  return all_codes
+
+myts = tokenise_audio(audio_array)
+start_tokens = torch.tensor([[ 128259]], dtype=torch.int64)
+end_tokens = torch.tensor([[128009, 128260, 128261, 128257]], dtype=torch.int64)
+final_tokens = torch.tensor([[128258, 128262]], dtype=torch.int64)
+voice_prompt = and_the_transcript_is
+prompt_tokked = tokenizer(voice_prompt, return_tensors="pt")
+
+input_ids = prompt_tokked["input_ids"]
+
+zeroprompt_input_ids = torch.cat([start_tokens, input_ids, end_tokens, torch.tensor([myts]), final_tokens], dim=1) # SOH SOT Text EOT EOH
+
+prompts = the_model_should_say
+
+all_modified_input_ids = []
+for prompt in prompts:
+  input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+  second_input_ids = torch.cat([zeroprompt_input_ids, start_tokens, input_ids, end_tokens], dim=1)
+  all_modified_input_ids.append(second_input_ids)
+
+
+all_padded_tensors = []
+all_attention_masks = []
+
+max_length = max([modified_input_ids.shape[1] for modified_input_ids in all_modified_input_ids])
+
+for modified_input_ids in all_modified_input_ids:
+  padding = max_length - modified_input_ids.shape[1]
+  padded_tensor = torch.cat([torch.full((1, padding), 128263, dtype=torch.int64), modified_input_ids], dim=1)
+  attention_mask = torch.cat([torch.zeros((1, padding), dtype=torch.int64), torch.ones((1, modified_input_ids.shape[1]), dtype=torch.int64)], dim=1)
+  all_padded_tensors.append(padded_tensor)
+  all_attention_masks.append(attention_mask)
+
+all_padded_tensors = torch.cat(all_padded_tensors, dim=0)
+all_attention_masks = torch.cat(all_attention_masks, dim=0)
+
+input_ids = all_padded_tensors.to(device)
+attention_mask = all_attention_masks.to(device)
+
+#@title Run Inference
+
+with torch.no_grad():
+  generated_ids = model.generate(
+      input_ids=input_ids,
+      # attention_mask=attention_mask,
+      max_new_tokens=1500,
+      do_sample=True,
+      temperature=0.5,
+      # top_k=40,
+      top_p=0.9,
+      repetition_penalty=1.1,
+      num_return_sequences=1,
+      eos_token_id=128258,
+      # end_token_id=128009
+  )
+
+# generated_ids = torch.cat([generated_ids, torch.tensor([[128262]]).to(device)], dim=1) # EOAI
+
+  #@title Convert output to speech
+  token_to_find = 128257
+  token_to_remove = 128258
+
+  # Check if the token exists in the tensor
+  token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)
+
+  if len(token_indices[1]) > 0:
+      last_occurrence_idx = token_indices[1][-1].item()
+      cropped_tensor = generated_ids[:, last_occurrence_idx+1:]
+  else:
+      cropped_tensor = generated_ids
+
+  mask = cropped_tensor != token_to_remove
+  processed_rows = []
+  for row in cropped_tensor:
+      # Apply the mask to each row
+      masked_row = row[row != token_to_remove]
+      processed_rows.append(masked_row)
+
+  code_lists = []
+  for row in processed_rows:
+      # row is a 1D tensor with its own length
+      row_length = row.size(0)
+      new_length = (row_length // 7) * 7  # largest multiple of 7 that fits in this row
+      trimmed_row = row[:new_length]
+      trimmed_row = [t - 128266 for t in trimmed_row]
+      code_lists.append(trimmed_row)
+
+  def redistribute_codes(code_list):
+    layer_1 = []
+    layer_2 = []
+    layer_3 = []
+    for i in range((len(code_list)+1)//7):
+      layer_1.append(code_list[7*i])
+      layer_2.append(code_list[7*i+1]-4096)
+      layer_3.append(code_list[7*i+2]-(2*4096))
+      layer_3.append(code_list[7*i+3]-(3*4096))
+      layer_2.append(code_list[7*i+4]-(4*4096))
+      layer_3.append(code_list[7*i+5]-(5*4096))
+      layer_3.append(code_list[7*i+6]-(6*4096))
+    codes = [torch.tensor(layer_1).unsqueeze(0),
+          torch.tensor(layer_2).unsqueeze(0),
+          torch.tensor(layer_3).unsqueeze(0)]
+    audio_hat = snac_model.decode(codes)
+    return audio_hat
+
+  my_samples = []
+  for code_list in code_lists:
+    samples = redistribute_codes(code_list)
+    my_samples.append(samples)
+
+
+
+    # Eğer soundfile yüklü değilse çalıştırın:
+# !pip install soundfile
+
+import soundfile as sf
+from IPython.display import Audio, display
+from google.colab import files
+
+for idx, samples in enumerate(my_samples):
+    # Tensörü NumPy dizisine çevir
+    audio = samples.detach().squeeze().cpu().numpy()
+    filename = f'audio_{idx}.wav'
+
+    # WAV dosyası olarak kaydet
+    sf.write(filename, audio, 24000)
+
+    # Ses oynatıcıyı göster
+    display(Audio(audio, rate=24000))
+
+    # İndir butonunu çalıştır
+    files.download(filename)

tr/orpheust-tts-base-fine-tune/config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.51.3",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.4.7",
+  "use_cache": true,
+  "vocab_size": 156940
+}

tr/orpheust-tts-base-fine-tune/generation_config.json

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+  "_from_model_config": true,
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+  "do_sample": true,
+  "eos_token_id": 128009,
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.51.3"
+}

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tr/orpheust-tts-base-fine-tune/source.txt

@@ -0,0 +1 @@
+https://huggingface.co/Cosmobillian/orpheust-tts-base-fine-tune

tr/orpheust-tts-base-fine-tune/special_tokens_map.json

@@ -0,0 +1,26 @@
+{
+  "additional_special_tokens": [
+    "<|audio|>"
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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tr/turkish_orpheus_tts/README.md

@@ -0,0 +1,304 @@
+---
+base_model: Karayakar/Orpheus-TTS-Turkish-PT-5000
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+license: apache-2.0
+language:
+- tr
+pipeline_tag: text-to-speech
+---
+
+# Uploaded  model
+
+- **Developed by:** Cosmobillian
+- **License:** apache-2.0
+- **Finetuned from model :** Karayakar/Orpheus-TTS-Turkish-PT-5000
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+
+
+
+inference.py
+(please install the necessary libraries)pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
+pip install snac pathlib torch transformers huggingface_hub librosa numpy scipy torchaudio Flask jsonify
+
+import os
+from snac import SNAC
+from pathlib import Path
+import torch
+from transformers import AutoModelForCausalLM, Trainer, TrainingArguments, AutoTokenizer,BitsAndBytesConfig
+from huggingface_hub import snapshot_download
+import librosa
+import numpy as np
+from scipy.io.wavfile import write
+import torchaudio
+from flask import Flask, jsonify, request
+
+modelLocalPath="Cosmobillian/turkish_orpheus_tts"
+
+
+def load_orpheus_tokenizer(model_id: str = modelLocalPath) -> AutoTokenizer:
+    tokenizer = AutoTokenizer.from_pretrained(model_id,local_files_only=True, device_map="cuda")
+    return tokenizer
+
+def load_snac():
+    snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+    return snac_model
+
+def load_orpheus_auto_model(model_id: str = modelLocalPath):
+    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16,local_files_only=True, device_map="cuda")
+    model.cuda()
+    return model
+
+
+
+def tokenize_audio(audio_file_path, snac_model):
+    audio_array, sample_rate = librosa.load(audio_file_path, sr=24000)
+    waveform = torch.from_numpy(audio_array).unsqueeze(0)
+    waveform = waveform.to(dtype=torch.float32)
+
+    waveform = waveform.unsqueeze(0)
+
+    with torch.inference_mode():
+        codes = snac_model.encode(waveform)
+
+    all_codes = []
+    for i in range(codes[0].shape[1]):
+        all_codes.append(codes[0][0][i].item() + 128266)
+        all_codes.append(codes[1][0][2 * i].item() + 128266 + 4096)
+        all_codes.append(codes[2][0][4 * i].item() + 128266 + (2 * 4096))
+        all_codes.append(codes[2][0][(4 * i) + 1].item() + 128266 + (3 * 4096))
+        all_codes.append(codes[1][0][(2 * i) + 1].item() + 128266 + (4 * 4096))
+        all_codes.append(codes[2][0][(4 * i) + 2].item() + 128266 + (5 * 4096))
+        all_codes.append(codes[2][0][(4 * i) + 3].item() + 128266 + (6 * 4096))
+
+    return all_codes
+
+
+def prepare_inputs(
+    fpath_audio_ref,
+    audio_ref_transcript: str,
+    text_prompts: list[str],
+    snac_model,
+    tokenizer,
+):
+
+    
+    start_tokens = torch.tensor([[128259]], dtype=torch.int64)
+    end_tokens = torch.tensor([[128009, 128260, 128261, 128257]], dtype=torch.int64)
+    final_tokens = torch.tensor([[128258, 128262]], dtype=torch.int64)
+
+
+    all_modified_input_ids = []
+    for prompt in text_prompts:
+        input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+        #second_input_ids = torch.cat([zeroprompt_input_ids, start_tokens, input_ids, end_tokens], dim=1)
+        second_input_ids = torch.cat([start_tokens, input_ids, end_tokens], dim=1)
+        all_modified_input_ids.append(second_input_ids)
+
+    all_padded_tensors = []
+    all_attention_masks = []
+    max_length = max([modified_input_ids.shape[1] for modified_input_ids in all_modified_input_ids])
+
+    for modified_input_ids in all_modified_input_ids:
+        padding = max_length - modified_input_ids.shape[1]
+        padded_tensor = torch.cat([torch.full((1, padding), 128263, dtype=torch.int64), modified_input_ids], dim=1)
+        attention_mask = torch.cat([torch.zeros((1, padding), dtype=torch.int64),
+                                    torch.ones((1, modified_input_ids.shape[1]), dtype=torch.int64)], dim=1)
+        all_padded_tensors.append(padded_tensor)
+        all_attention_masks.append(attention_mask)
+
+    all_padded_tensors = torch.cat(all_padded_tensors, dim=0)
+    all_attention_masks = torch.cat(all_attention_masks, dim=0)
+
+    input_ids = all_padded_tensors.to("cuda")
+    attention_mask = all_attention_masks.to("cuda")
+    return input_ids, attention_mask
+
+
+
+def inference(model, input_ids, attention_mask):
+    with torch.no_grad():
+        generated_ids = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            max_new_tokens=2048,
+            do_sample=True,
+            temperature=0.2,
+            top_k=10,
+            top_p=0.9,
+            repetition_penalty=1.9,
+            num_return_sequences=1,
+            eos_token_id=128258,
+
+        )
+
+        generated_ids = torch.cat([generated_ids, torch.tensor([[128262]]).to("cuda")], dim=1) # EOAI
+
+        return generated_ids
+
+
+def convert_tokens_to_speech(generated_ids, snac_model):
+    token_to_find = 128257
+    token_to_remove = 128258
+    token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)
+
+    if len(token_indices[1]) > 0:
+        last_occurrence_idx = token_indices[1][-1].item()
+        cropped_tensor = generated_ids[:, last_occurrence_idx + 1:]
+    else:
+        cropped_tensor = generated_ids
+
+    _mask = cropped_tensor != token_to_remove
+    processed_rows = []
+    for row in cropped_tensor:
+        masked_row = row[row != token_to_remove]
+        processed_rows.append(masked_row)
+
+    code_lists = []
+    for row in processed_rows:
+        row_length = row.size(0)
+        new_length = (row_length // 7) * 7 
+        trimmed_row = row[:new_length]
+        trimmed_row = [t - 128266 for t in trimmed_row]
+        code_lists.append(trimmed_row)
+
+    my_samples = []
+    for code_list in code_lists:
+        samples = redistribute_codes(code_list, snac_model)
+        my_samples.append(samples)
+
+    return my_samples
+
+
+def redistribute_codes(code_list, snac_model):
+    layer_1 = []
+    layer_2 = []
+    layer_3 = []
+
+    for i in range((len(code_list) + 1) // 7):
+        layer_1.append(code_list[7 * i])
+        layer_2.append(code_list[7 * i + 1] - 4096)
+        layer_3.append(code_list[7 * i + 2] - (2 * 4096))
+        layer_3.append(code_list[7 * i + 3] - (3 * 4096))
+        layer_2.append(code_list[7 * i + 4] - (4 * 4096))
+        layer_3.append(code_list[7 * i + 5] - (5 * 4096))
+        layer_3.append(code_list[7 * i + 6] - (6 * 4096))
+
+    codes = [
+        torch.tensor(layer_1).unsqueeze(0),
+        torch.tensor(layer_2).unsqueeze(0),
+        torch.tensor(layer_3).unsqueeze(0)
+    ]
+    audio_hat = snac_model.decode(codes)
+    return audio_hat 
+
+
+def to_wav_from(samples: list) -> list[np.ndarray]:
+    """Converts a list of PyTorch tensors (or NumPy arrays) to NumPy arrays."""
+    processed_samples = []
+
+    for s in samples:
+        if isinstance(s, torch.Tensor):  
+            s = s.detach().squeeze().to('cpu').numpy()
+        else:  
+            s = np.squeeze(s)
+
+        processed_samples.append(s)
+
+    return processed_samples
+
+
+def zero_shot_tts(fpath_audio_ref, audio_ref_transcript, texts: list[str], model, snac_model, tokenizer):
+    print(f"fpath_audio_ref {fpath_audio_ref}")
+    print(f"audio_ref_transcript {audio_ref_transcript}")
+    print(f"texts {texts}")
+    inp_ids, attn_mask = prepare_inputs(fpath_audio_ref, audio_ref_transcript, texts, snac_model, tokenizer)
+    print(f"input_id_len:{len(inp_ids)}")
+    gen_ids = inference(model, inp_ids, attn_mask)
+    samples = convert_tokens_to_speech(gen_ids, snac_model)
+    wav_forms = to_wav_from(samples)
+    return wav_forms
+
+
+def save_wav(samples: list[np.array], sample_rate: int, filenames: list[str]):
+    """ Saves a list of tensors as .wav files.
+
+    Args:
+        samples (list[torch.Tensor]): List of audio tensors.
+        sample_rate (int): Sample rate in Hz.
+        filenames (list[str]): List of filenames to save.
+    """
+    wav_data = to_wav_from(samples)
+
+    for data, filename in zip(wav_data, filenames):
+        write(filename, sample_rate, data.astype(np.float32))
+        print(f"saved to {filename}")
+
+
+def get_ref_audio_and_transcript(root_folder: str):
+    root_path = Path(root_folder)
+    print(f"root_path   {root_path}")
+    out = []
+    for speaker_folder in root_path.iterdir():
+        if speaker_folder.is_dir():  # Ensure it's a directory
+            wav_files = list(speaker_folder.glob("*.wav"))
+            txt_files = list(speaker_folder.glob("*.txt"))
+
+            if wav_files and txt_files:
+                ref_audio = wav_files[0]  # Assume only one .wav file per folder
+                transcript = txt_files[0].read_text(encoding="utf-8").strip()
+                out.append((ref_audio, transcript))
+
+    return out
+
+app = Flask(__name__)
+
+
+@app.route('/generate', methods=['POST'])
+def generate():
+    content = request.json
+    process_data(content)
+    rresponse = {
+        'received': content,
+        'status': 'success'
+    }
+    response= jsonify(rresponse)
+    response.headers['Content-Type'] = 'application/json; charset=utf-8'
+    return response
+
+
+
+def process_data(jsonText):
+    texts = [f"{jsonText['text']}"]
+    #print(f"texts:{texts}")
+    #print(f"prompt_pairs:{prompt_pairs}")
+    for fpath_audio, audio_transcript in prompt_pairs:
+        print(f"zero shot: {fpath_audio} {audio_transcript}")
+        wav_forms = zero_shot_tts(fpath_audio, audio_transcript, texts, model, snac_model, tokenizer)
+
+        import os
+        from pathlib import Path
+        from datetime import datetime
+        out_dir = Path(fpath_audio).parent / "inference"
+        #print(f"out_dir:{out_dir}")
+        out_dir.mkdir(parents=True, exist_ok=True)  #
+        timestamp_str = str(int(datetime.now().timestamp()))
+        file_names = [f"{out_dir.as_posix()}/{Path(fpath_audio).stem}_{i}_{timestamp_str}.wav" for i, t in enumerate(texts)]
+        #print(f"file_names:{file_names}")
+        save_wav(wav_forms, 24000, file_names)
+        
+      
+
+if __name__ == "__main__":
+    tokenizer = load_orpheus_tokenizer()
+    model = load_orpheus_auto_model()
+    snac_model = load_snac()
+    prompt_pairs = get_ref_audio_and_transcript("D:\\AI_APPS\\Orpheus-TTS\\data")
+    print(f"snac_model loaded")
+    app.run(debug=True,port=5400)

tr/turkish_orpheus_tts/config.json

@@ -0,0 +1,37 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.51.3",
+  "unsloth_version": "2025.4.7",
+  "use_cache": true,
+  "vocab_size": 156940
+}

tr/turkish_orpheus_tts/generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.51.3"
+}

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tr/turkish_orpheus_tts/source.txt

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tr/turkish_orpheus_tts/special_tokens_map.json

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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tr/turkish_orpheus_tts/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc3fecb199b4170636dbfab986d25f628157268d37b861f9cadaca60b1353bce
+size 22849547