ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter8/orpheus/orpheus_sft_unsloth.py

@@ -0,0 +1,509 @@
+# -*- coding: utf-8 -*-
+"""Orpheus_(3B)-TTS.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb
+
+To run this, press "*Runtime*" and press "*Run all*" on a **free** Tesla T4 Google Colab instance!
+<div class="align-center">
+<a href="https://unsloth.ai/"><img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="115"></a>
+<a href="https://discord.gg/unsloth"><img src="https://github.com/unslothai/unsloth/raw/main/images/Discord button.png" width="145"></a>
+<a href="https://docs.unsloth.ai/"><img src="https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true" width="125"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href="https://github.com/unslothai/unsloth">Github</a> </i> ⭐
+</div>
+
+To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).
+
+You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)
+
+### News
+
+Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).
+
+[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!
+
+Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.
+
+Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).
+
+Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).
+
+### Installation
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%capture
+# import os, re
+# if "COLAB_" not in "".join(os.environ.keys()):
+#     !pip install unsloth
+# else:
+#     # Do this only in Colab notebooks! Otherwise use pip install unsloth
+#     import torch; v = re.match(r"[0-9\.]{3,}", str(torch.__version__)).group(0)
+#     xformers = "xformers==" + ("0.0.32.post2" if v == "2.8.0" else "0.0.29.post3")
+#     !pip install --no-deps bitsandbytes accelerate {xformers} peft trl triton cut_cross_entropy unsloth_zoo
+#     !pip install sentencepiece protobuf "datasets>=3.4.1,<4.0.0" "huggingface_hub>=0.34.0" hf_transfer
+#     !pip install --no-deps unsloth
+# !pip install transformers==4.55.4
+# !pip install --no-deps trl==0.22.2
+# !pip install snac
+# !pip install soundfile librosa
+
+"""### Unsloth
+
+`FastModel` supports loading nearly any model now! This includes Vision and Text models!
+
+Thank you to [Etherl](https://huggingface.co/Etherll) for creating this notebook!
+"""
+
+from unsloth import FastLanguageModel
+import torch
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/orpheus-3b-0.1-ft",
+    max_seq_length= 2048, # Choose any for long context!
+    dtype = None, # Select None for auto detection
+    load_in_4bit = False, # Select True for 4bit which reduces memory usage
+)
+
+"""We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"""
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 64, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 64,
+    lora_dropout = 0, # Supports any, but = 0 is optimized
+    bias = "none",    # Supports any, but = "none" is optimized
+    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+    random_state = 42,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+)
+
+"""<a name="Data"></a>
+### Data Prep  
+
+We will use the `MrDragonFox/Elise`, which is designed for training TTS models. Ensure that your dataset follows the required format: **text, audio** for single-speaker models or **source, text, audio** for multi-speaker models. You can modify this section to accommodate your own dataset, but maintaining the correct structure is essential for optimal training.
+"""
+
+from datasets import load_dataset
+dataset = load_dataset(
+    "maxbsoft/mrdragonfox-elise",
+    revision="2cc657c3f94a83df18fcd968b7531ca1a19c7f88",
+    split="train",
+)
+
+#@title Tokenization Function
+
+import locale
+import torchaudio.transforms as T
+import os
+import torch
+from snac import SNAC
+locale.getpreferredencoding = lambda: "UTF-8"
+ds_sample_rate = dataset[0]["audio"]["sampling_rate"]
+
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+snac_model = snac_model.to("cuda")
+def tokenise_audio(waveform):
+  waveform = torch.from_numpy(waveform).unsqueeze(0)
+  waveform = waveform.to(dtype=torch.float32)
+  resample_transform = T.Resample(orig_freq=ds_sample_rate, new_freq=24000)
+  waveform = resample_transform(waveform)
+
+  waveform = waveform.unsqueeze(0).to("cuda")
+
+  #generate the codes from snac
+  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 add_codes(example):
+    # Always initialize codes_list to None
+    codes_list = None
+
+    try:
+        answer_audio = example.get("audio")
+        # If there's a valid audio array, tokenise it
+        if answer_audio and "array" in answer_audio:
+            audio_array = answer_audio["array"]
+            codes_list = tokenise_audio(audio_array)
+    except Exception as e:
+        print(f"Skipping row due to error: {e}")
+        # Keep codes_list as None if we fail
+    example["codes_list"] = codes_list
+
+    return example
+
+dataset = dataset.map(add_codes, remove_columns=["audio"])
+
+tokeniser_length = 128256
+start_of_text = 128000
+end_of_text = 128009
+
+start_of_speech = tokeniser_length + 1
+end_of_speech = tokeniser_length + 2
+
+start_of_human = tokeniser_length + 3
+end_of_human = tokeniser_length + 4
+
+start_of_ai = tokeniser_length + 5
+end_of_ai =  tokeniser_length + 6
+pad_token = tokeniser_length + 7
+
+audio_tokens_start = tokeniser_length + 10
+
+dataset = dataset.filter(lambda x: x["codes_list"] is not None)
+dataset = dataset.filter(lambda x: len(x["codes_list"]) > 0)
+
+def remove_duplicate_frames(example):
+    vals = example["codes_list"]
+    if len(vals) % 7 != 0:
+        raise ValueError("Input list length must be divisible by 7")
+
+    result = vals[:7]
+
+    removed_frames = 0
+
+    for i in range(7, len(vals), 7):
+        current_first = vals[i]
+        previous_first = result[-7]
+
+        if current_first != previous_first:
+            result.extend(vals[i:i+7])
+        else:
+            removed_frames += 1
+
+    example["codes_list"] = result
+
+    return example
+
+dataset = dataset.map(remove_duplicate_frames)
+
+tok_info = '''*** HERE you can modify the text prompt
+If you are training a multi-speaker model (e.g., canopylabs/orpheus-3b-0.1-ft),
+ensure that the dataset includes a "source" field and format the input accordingly:
+- Single-speaker: f"{example['text']}"
+- Multi-speaker: f"{example['source']}: {example['text']}"
+'''
+print(tok_info)
+
+def create_input_ids(example):
+    # Determine whether to include the source field
+    text_prompt = f"{example['source']}: {example['text']}" if "source" in example else example["text"]
+
+    text_ids = tokenizer.encode(text_prompt, add_special_tokens=True)
+    text_ids.append(end_of_text)
+
+    example["text_tokens"] = text_ids
+    input_ids = (
+        [start_of_human]
+        + example["text_tokens"]
+        + [end_of_human]
+        + [start_of_ai]
+        + [start_of_speech]
+        + example["codes_list"]
+        + [end_of_speech]
+        + [end_of_ai]
+    )
+    example["input_ids"] = input_ids
+    example["labels"] = input_ids
+    example["attention_mask"] = [1] * len(input_ids)
+
+    return example
+
+
+dataset = dataset.map(create_input_ids, remove_columns=["text", "codes_list"])
+columns_to_keep = ["input_ids", "labels", "attention_mask"]
+columns_to_remove = [col for col in dataset.column_names if col not in columns_to_keep]
+
+dataset = dataset.remove_columns(columns_to_remove)
+
+"""<a name="Train"></a>
+### Train the model
+Now let's use Huggingface  `Trainer`! More docs here: [Transformers docs](https://huggingface.co/docs/transformers/main_classes/trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`.
+
+**Note:** Using a per_device_train_batch_size >1 may lead to errors if multi-GPU setup to avoid issues, ensure CUDA_VISIBLE_DEVICES is set to a single GPU (e.g., CUDA_VISIBLE_DEVICES=0).
+"""
+
+from transformers import TrainingArguments,Trainer,DataCollatorForSeq2Seq
+trainer = Trainer(
+    model = model,
+    train_dataset = dataset,
+    args = TrainingArguments(
+        per_device_train_batch_size = 1,
+        gradient_accumulation_steps = 4,
+        warmup_steps = 5,
+        num_train_epochs = 1, # Set this for 1 full training run.
+        learning_rate = 2e-4,
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        weight_decay = 0.01,
+        lr_scheduler_type = "linear",
+        seed = 42,
+        output_dir = "outputs",
+        report_to = "none", # Use this for WandB etc
+    ),
+)
+
+# @title Show current memory stats
+gpu_stats = torch.cuda.get_device_properties(0)
+start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
+max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
+print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
+print(f"{start_gpu_memory} GB of memory reserved.")
+
+trainer_stats = trainer.train()
+
+# @title Show final memory and time stats
+used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
+used_memory_for_lora = round(used_memory - start_gpu_memory, 3)
+used_percentage = round(used_memory / max_memory * 100, 3)
+lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)
+print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.")
+print(
+    f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training."
+)
+print(f"Peak reserved memory = {used_memory} GB.")
+print(f"Peak reserved memory for training = {used_memory_for_lora} GB.")
+print(f"Peak reserved memory % of max memory = {used_percentage} %.")
+print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.")
+
+
+print("Saving model...")
+"""<a name="Save"></a>
+### Saving, loading finetuned models
+To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.
+
+**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!
+"""
+
+model.save_pretrained("lora_model")  # Local saving
+tokenizer.save_pretrained("lora_model")
+# model.push_to_hub("your_name/lora_model", token = "...") # Online saving
+# tokenizer.push_to_hub("your_name/lora_model", token = "...") # Online saving
+
+"""### Saving to float16
+
+We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens.
+"""
+
+# Merge to 16bit
+if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",)
+if False: model.push_to_hub_merged("hf/model", tokenizer, save_method = "merged_16bit", token = "")
+
+# Merge to 4bit
+if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",)
+if False: model.push_to_hub_merged("hf/model", tokenizer, save_method = "merged_4bit", token = "")
+
+# Just LoRA adapters
+if False:
+    model.save_pretrained("model")
+    tokenizer.save_pretrained("model")
+if False:
+    model.push_to_hub("hf/model", token = "")
+    tokenizer.push_to_hub("hf/model", token = "")
+
+print("Inference...")
+"""<a name="Inference"></a>
+### Inference
+Let's run the model! You can change the prompts
+
+
+"""
+
+prompts = [
+    "Hey there my name is Elise, <giggles> and I'm a speech generation model that can sound like a person.",
+    "I missed you <laugh> so much! It's been way too long.",
+    "This is absolutely amazing <gasp> I can't believe it worked!",
+]
+
+# Orpheus supports emotion tags: <laugh>, <giggles>, <chuckle>, <sigh>,
+# <gasp>, <yawn>, <cough>, <sniffle>, <groan>, etc.
+chosen_voice = None # None for single-speaker
+
+#@title Run Inference
+
+
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+# Moving snac_model cuda to cpu
+snac_model.to("cpu")
+
+prompts_ = [(f"{chosen_voice}: " + p) if chosen_voice else p for p in prompts]
+
+all_input_ids = []
+
+for prompt in prompts_:
+  input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+  all_input_ids.append(input_ids)
+
+start_token = torch.tensor([[ 128259]], dtype=torch.int64) # Start of human
+end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64) # End of text, End of human
+
+all_modified_input_ids = []
+for input_ids in all_input_ids:
+  modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1) # SOH SOT Text EOT EOH
+  all_modified_input_ids.append(modified_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")
+generated_ids = model.generate(
+      input_ids=input_ids,
+      attention_mask=attention_mask,
+      max_new_tokens=1200,
+      do_sample=True,
+      temperature=0.6,
+      top_p=0.95,
+      repetition_penalty=1.1,
+      num_return_sequences=1,
+      eos_token_id=128258,
+     use_cache = True
+  )
+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)
+
+
+def redistribute_codes(code_list):
+  layer_1 = []
+  layer_2 = []
+  layer_3 = []
+  # SNAC codebook size is 4096 per layer (valid range: 0-4095)
+  max_code_value = 4095
+  for i in range((len(code_list)+1)//7):
+    # Extract codes with offsets
+    c0 = code_list[7*i]
+    c1 = code_list[7*i+1]-4096
+    c2 = code_list[7*i+2]-(2*4096)
+    c3 = code_list[7*i+3]-(3*4096)
+    c4 = code_list[7*i+4]-(4*4096)
+    c5 = code_list[7*i+5]-(5*4096)
+    c6 = code_list[7*i+6]-(6*4096)
+    
+    # Check if any code is out of valid range
+    # If so, terminate audio generation to avoid machine noise
+    if (c0 < 0 or c0 > max_code_value or
+        c1 < 0 or c1 > max_code_value or
+        c2 < 0 or c2 > max_code_value or
+        c3 < 0 or c3 > max_code_value or
+        c4 < 0 or c4 > max_code_value or
+        c5 < 0 or c5 > max_code_value or
+        c6 < 0 or c6 > max_code_value):
+      print(f"Invalid audio code detected at frame {i}, terminating audio generation")
+      break
+    
+    layer_1.append(c0)
+    layer_2.append(c1)
+    layer_3.append(c2)
+    layer_3.append(c3)
+    layer_2.append(c4)
+    layer_3.append(c5)
+    layer_3.append(c6)
+  
+  # Return empty/silent audio if no valid codes were found
+  if not layer_1:
+    print("Warning: No valid audio codes found, returning silence")
+    return torch.zeros(1, 1, 1000)  # Small silent audio
+  
+  codes = [torch.tensor(layer_1, dtype=torch.long).unsqueeze(0),
+         torch.tensor(layer_2, dtype=torch.long).unsqueeze(0),
+         torch.tensor(layer_3, dtype=torch.long).unsqueeze(0)]
+
+  # codes = [c.to("cuda") for c in codes]
+  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)
+
+# Save generated audio samples to WAV files
+import soundfile as sf
+import os
+
+output_dir = "generated_audio"
+os.makedirs(output_dir, exist_ok=True)
+
+for idx, audio_sample in enumerate(my_samples):
+  # Convert tensor to numpy array and squeeze to remove batch dimension
+  # Detach from computation graph to avoid gradient tracking error
+  audio_numpy = audio_sample.squeeze().detach().cpu().numpy()
+  # Save to WAV file with 24kHz sample rate (matching SNAC model)
+  output_path = os.path.join(output_dir, f"output_{idx}.wav")
+  sf.write(output_path, audio_numpy, 24000)
+  print(f"Saved audio to {output_path}")
+
+# Clean up to save RAM
+del my_samples,samples
+
+"""And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!
+
+Some other links:
+1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)
+2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)
+3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)
+6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!
+
+<div class="align-center">
+  <a href="https://unsloth.ai"><img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="115"></a>
+  <a href="https://discord.gg/unsloth"><img src="https://github.com/unslothai/unsloth/raw/main/images/Discord.png" width="145"></a>
+  <a href="https://docs.unsloth.ai/"><img src="https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true" width="125"></a>
+
+  Join Discord if you need help + ⭐️ <i>Star us on <a href="https://github.com/unslothai/unsloth">Github</a> </i> ⭐️
+</div>
+
+"""