README.md

# Finetuning LLMs to output audio

In this example, we finetune Orpcanopylabs/orpheus-tts-0.1-pretrained (a LLaMA 3.2 3b model) to output audio.

The `finetune.yml` withe current settings will run on any Nvidia GPU with 45GB VRAM or more. If you adjust the batch size it can easily run on any GPU under 24GB.

## Dataset pre-processing for pre-training
If you are adding another voice in English, please jump ahead to finetuning pre-processing.

For this to work, we need to preprocess our dataset. Since we are expecting to output audio, we will need to add tokens to the tokenizer.

Using this code, it will download the SNAC model and add the correct tokens and upload the final dataset.

```python
import torch
from snac import SNAC
from datasets import load_dataset
from huggingface_hub import snapshot_download
from datasets import load_dataset
import random
import torchaudio.transforms as T
from transformers import AutoTokenizer
import os

my_original_dataset_name = "<huggingface-id-of-dataset-that-we-want-to-preprocess>"
name_to_push_dataset_to = "<huggingface-id-of-where-to-save-dataset>"

dsn = my_original_dataset_name

snapshot_download(
    repo_id=dsn,
    repo_type="dataset",
    revision="main",
    max_workers=64,
)


ds = load_dataset(dsn, split="train")
ds_sample_rate = ds[0]["audio"]["sampling_rate"]

model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
model = model.to("mps")

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 = 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

ds = ds.map(add_codes, remove_columns=["audio"])

#@title Load Tokenizer
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

tokenizer_name = "canopylabs/orpheus-3b-0.1-pretrained"


tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
num_proc = os.cpu_count() - 2

ds = ds.filter(lambda x: x["codes_list"] is not None)
ds = ds.filter(lambda x: len(x["codes_list"]) > 0)

#@title Create Input Ids
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

ds = ds.map(remove_duplicate_frames, num_proc=num_proc)


def create_input_ids(example):
    text_ids = tokenizer.encode({example['text']},  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

ds = ds.map(create_input_ids, num_proc=num_proc, remove_columns=["text", "codes_list"])

#@title Remove unnecessary columns
columns_to_keep = ["input_ids", "labels", "attention_mask"]
columns_to_remove = [col for col in ds.column_names if col not in columns_to_keep]

ds = ds.remove_columns(columns_to_remove)

ds.push_to_hub(name_to_push_dataset_to)
```


## Finetune pre-processing
Use this code to add a new voice.

```python
import torch
from snac import SNAC
from datasets import load_dataset
from huggingface_hub import snapshot_download
from datasets import load_dataset
import random
import torchaudio.transforms as T
from transformers import AutoTokenizer
import os

my_original_dataset_name = "<huggingface-id-of-dataset-that-we-want-to-preprocess>"
name_to_push_dataset_to = "<huggingface-id-of-where-to-save-dataset>"

dsn = my_original_dataset_name

snapshot_download(
    repo_id=dsn,
    repo_type="dataset",
    revision="main",
    max_workers=64,
)


ds = load_dataset(dsn, split="train")
ds_sample_rate = ds[0]["audio"]["sampling_rate"]

model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
model = model.to("mps")

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 = 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

ds = ds.map(add_codes, remove_columns=["audio"])

#@title Load Tokenizer
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

tokenizer_name = "canopylabs/orpheus-3b-0.1-pretrained"


tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
num_proc = os.cpu_count() - 2

ds = ds.filter(lambda x: x["codes_list"] is not None)
ds = ds.filter(lambda x: len(x["codes_list"]) > 0)

#@title Create Input Ids
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

ds = ds.map(remove_duplicate_frames, num_proc=num_proc)

tok_info = '''*** HERE you can modify the text prompt
i.e. if you wanted a multispeaker model like canopylabs/orpheus-3b-0.1-ft, you can pass:
f"{example["source"]}:  {example["text"]}", as is passed.
'''
print(tok_info)

def create_input_ids(example):
    text_ids = tokenizer.encode(f"{example['speaker_id']}: {example['text']}",  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

ds = ds.map(create_input_ids, num_proc=num_proc, remove_columns=["text", "codes_list"])

#@title Remove unnecessary columns
columns_to_keep = ["input_ids", "labels", "attention_mask"]
columns_to_remove = [col for col in ds.column_names if col not in columns_to_keep]

ds = ds.remove_columns(columns_to_remove)

ds.push_to_hub(name_to_push_dataset_to)
```

## Training
After preprocessing is done, fill out the blanks in finetune.yml and simply run `axolotl train finetune.yml`

## Inference
For inference, please refer to the original [orpheus github](https://github.com/canopyai/Orpheus-TTS/tree/main).