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Model: hypaai/Hypa-Orpheus-Step-latest-16bit Source: Original Platform
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36
.gitattributes
vendored
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vendored
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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21
README.md
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21
README.md
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---
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base_model: unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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license: apache-2.0
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language:
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- en
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---
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# Uploaded finetuned model
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- **Developed by:** hypaai
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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93
chat_template.jinja
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93
chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{{- "<|eot_id|>" }}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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37
config.json
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37
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"torch_dtype": "bfloat16",
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"eos_token_id": 128009,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 24,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_theta": 500000.0,
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"rope_type": "llama3"
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},
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"tie_word_embeddings": true,
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"unsloth_fixed": true,
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"unsloth_version": "2026.7.3",
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"use_cache": true,
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"vocab_size": 156940
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}
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11
generation_config.json
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": 128009,
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"max_length": 131072,
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"pad_token_id": 128004,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "5.13.1"
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}
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330
handler.py
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330
handler.py
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"""
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HF Inference Endpoint handler — Hypa Orpheus TTS + Voice Cloning (merged 16-bit).
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Task matrix (routed by `parameters`):
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task="tts", mode="vanilla" : {speaker}: text -> speech
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task="tts", mode="translate" : {speaker} - {Language}: text -> speech in Language
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task="vc", mode="vanilla" : reference (text+audio) + target text -> speech in reference voice
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task="vc", mode="translate" : + language tag on target text -> cross-lingual cloning
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VC method="m1" (in-context) | method="m2" (continue-speaking)
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Output parity with the legacy endpoint: `audio_b64` is base64 of the RAW
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float32 little-endian mono PCM buffer at 24000 Hz (NO WAV/RIFF container),
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so existing products decode with: np.frombuffer(base64.b64decode(s), dtype=np.float32)
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[If the legacy endpoint used int16, change RAW_DTYPE to np.int16 below.]
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Prompts are byte-identical to Step-III training (_encode_text / build_tts /
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build_vc_both), reference codes are frame-deduped, and prompts reach vLLM as
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token ids (never a decoded string).
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"""
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import io
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import os
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import base64
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import tempfile
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import traceback
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import numpy as np
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import torch
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import soundfile as sf
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import librosa
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from transformers import AutoTokenizer
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from snac import SNAC
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from vllm import LLM, SamplingParams
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class EndpointHandler:
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# ---- Orpheus special tokens (fixed by the model) ----
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TOKENISER_LEN = 128256
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START_OF_TEXT = 128000
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END_OF_TEXT = 128009
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START_OF_SPEECH = TOKENISER_LEN + 1 # 128257
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END_OF_SPEECH = TOKENISER_LEN + 2 # 128258
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START_OF_HUMAN = TOKENISER_LEN + 3 # 128259
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END_OF_HUMAN = TOKENISER_LEN + 4 # 128260
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START_OF_AI = TOKENISER_LEN + 5 # 128261
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END_OF_AI = TOKENISER_LEN + 6 # 128262
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AUDIO_OFFSET = 128266
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# NOTE: fine-tune data capped at 2048 tokens; 4096 kept so M1-VC prompts
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# (ref codes + two texts, often 1000-2000 tokens) retain a generation
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# budget. Base Llama-3 RoPE supports these positions natively; expect the
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# best quality when prompt+generation stays near the trained ~2048.
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MAX_MODEL_LEN = 4096
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MAX_REF_SECONDS = 30
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SNAC_SR = 24000
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RAW_DTYPE = np.float32 # legacy raw-PCM dtype (see docstring)
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LANG_DISPLAY = {
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"en": "English", "es": "Spanish", "fr": "French", "ha": "Hausa",
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"yo": "Yoruba", "sw": "Swahili", "ar": "Arabic", "pt": "Portuguese",
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"ann": "Annang", "ebi": "Ebira", "efi": "Efik", "ego": "Eggon",
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"urh": "Urhobo", "ibb": "Ibibio", "idm": "Idoma", "igl": "Igala",
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"ig": "Igbo", "nup": "Nupe", "tiv": "Tiv", "pg": "Pidgin",
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}
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# ------------------------------------------------------------------ init
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def __init__(self, path=""):
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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# SNAC first (tiny, ~80 MB) so it never contends with vLLM's reservation.
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self.snac = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to(self.device).eval()
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self.model = LLM(
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path,
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max_model_len=self.MAX_MODEL_LEN,
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gpu_memory_utilization=0.75,
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)
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self.tokenizer = AutoTokenizer.from_pretrained(path)
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# ------------------------------------------------------- text encoding
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def _lang_display(self, x):
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if x is None:
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return None
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k = str(x).strip().lower()
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return self.LANG_DISPLAY.get(k, k.capitalize() if k else None)
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def _encode_text(self, text, speaker=None, lang_tag=None, add_bos=True):
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text = "" if text is None else str(text).strip()
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spk = speaker if (speaker and str(speaker).strip().lower() not in ("", "random", "none")) else None
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if spk and lang_tag:
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prompt = f"{spk} - {lang_tag}: {text}"
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elif spk:
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prompt = f"{spk}: {text}"
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elif lang_tag:
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prompt = f"{lang_tag}: {text}"
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else:
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prompt = text
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ids = self.tokenizer.encode(prompt, add_special_tokens=add_bos)
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ids.append(self.END_OF_TEXT)
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return ids
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# ------------------------------------------------------ audio encoding
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def _b64_to_wave(self, b64_str):
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raw = base64.b64decode(b64_str)
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if not raw:
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raise ValueError("reference_audio is empty.")
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try:
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arr, sr = sf.read(io.BytesIO(raw), dtype="float32")
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except Exception:
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# temp-file fallback: librosa/audioread handles containers
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# libsndfile can't open, but needs a real file path for some codecs.
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tmp = None
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try:
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with tempfile.NamedTemporaryFile(delete=False, suffix=".audio") as f:
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f.write(raw)
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tmp = f.name
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arr, sr = librosa.load(tmp, sr=None, mono=False)
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arr = np.asarray(arr, dtype=np.float32)
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if arr.ndim > 1:
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arr = arr.T # librosa returns (ch, n)
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finally:
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if tmp and os.path.exists(tmp):
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os.remove(tmp)
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if arr.ndim > 1:
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arr = arr.mean(axis=1)
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if arr.size == 0 or not np.isfinite(arr).all():
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raise ValueError("Reference audio is empty or contains invalid samples.")
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if sr != self.SNAC_SR:
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arr = librosa.resample(arr.astype(np.float32), orig_sr=sr, target_sr=self.SNAC_SR)
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dur = len(arr) / self.SNAC_SR
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if dur > self.MAX_REF_SECONDS:
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raise ValueError(f"Reference audio is {dur:.1f}s; max is {self.MAX_REF_SECONDS}s. "
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f"Send a shorter clip.")
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return arr.astype(np.float32)
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@torch.inference_mode()
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def _audio_to_codes(self, arr):
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wav = torch.from_numpy(arr).to(self.device)[None, None]
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codes = self.snac.encode(wav)
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c0, c1, c2 = codes[0][0].tolist(), codes[1][0].tolist(), codes[2][0].tolist()
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n = min(len(c0), len(c1) // 2, len(c2) // 4)
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out = []
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for i in range(n):
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out += [
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c0[i] + self.AUDIO_OFFSET,
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c1[2 * i] + self.AUDIO_OFFSET + 4096,
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c2[4 * i] + self.AUDIO_OFFSET + 2 * 4096,
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c2[4 * i + 1] + self.AUDIO_OFFSET + 3 * 4096,
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c1[2 * i + 1] + self.AUDIO_OFFSET + 4 * 4096,
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c2[4 * i + 2] + self.AUDIO_OFFSET + 5 * 4096,
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c2[4 * i + 3] + self.AUDIO_OFFSET + 6 * 4096,
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]
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return out
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@staticmethod
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def _dedup_frames(codes):
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if not codes:
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||||
return codes
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codes = codes[: (len(codes) // 7) * 7]
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if len(codes) < 7:
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return codes
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||||
result = codes[:7]
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||||
for i in range(7, len(codes), 7):
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||||
if codes[i] != result[-7]:
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result.extend(codes[i:i + 7])
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return result
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# ------------------------------------------------------ prompt builders
|
||||
def build_tts_prompt(self, text, speaker, mode, language):
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lang_tag = self._lang_display(language) if mode == "translate" else None
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tt = self._encode_text(text, speaker, lang_tag, add_bos=True)
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return [self.START_OF_HUMAN] + tt + [self.END_OF_HUMAN,
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self.START_OF_AI, self.START_OF_SPEECH]
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def build_vc_prompt(self, ref_text, ref_codes, target_text, mode, language, method):
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tag2 = self._lang_display(language) if mode == "translate" else None
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tt1 = self._encode_text(ref_text, None, None, add_bos=True)
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tt2 = self._encode_text(target_text, None, tag2, add_bos=False)
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if method == "m1":
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return ([self.START_OF_HUMAN] + tt1 + [self.END_OF_HUMAN,
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||||
self.START_OF_AI, self.START_OF_SPEECH] + ref_codes +
|
||||
[self.END_OF_SPEECH, self.END_OF_AI,
|
||||
self.START_OF_HUMAN] + tt2 + [self.END_OF_HUMAN,
|
||||
self.START_OF_AI, self.START_OF_SPEECH])
|
||||
return ([self.START_OF_HUMAN] + tt1 + tt2 + [self.END_OF_HUMAN,
|
||||
self.START_OF_AI, self.START_OF_SPEECH] + ref_codes)
|
||||
|
||||
# --------------------------------------------------------- generation
|
||||
def _generate(self, prompt_ids, params):
|
||||
sampling = SamplingParams(
|
||||
temperature = params["temperature"],
|
||||
top_p = params["top_p"],
|
||||
top_k = params["top_k"],
|
||||
max_tokens = params["max_new_tokens"],
|
||||
repetition_penalty = params["repetition_penalty"],
|
||||
stop_token_ids = [self.END_OF_SPEECH, self.END_OF_AI],
|
||||
detokenize = False,
|
||||
)
|
||||
outputs = self.model.generate({"prompt_token_ids": prompt_ids}, sampling)
|
||||
return list(outputs[0].outputs[0].token_ids)
|
||||
|
||||
# ----------------------------------------------------------- decoding
|
||||
@torch.inference_mode()
|
||||
def _codes_to_wave(self, gen_ids):
|
||||
"""Frame-validating SNAC decode: accepts only well-formed 7-token frames
|
||||
(token k in slot-k range), resyncs on malformed spans."""
|
||||
frames, i, n, resyncs = [], 0, len(gen_ids), 0
|
||||
while i <= n - 7:
|
||||
vals, ok = [], True
|
||||
for k in range(7):
|
||||
lo = self.AUDIO_OFFSET + k * 4096
|
||||
t = gen_ids[i + k]
|
||||
if not (lo <= t < lo + 4096):
|
||||
ok = False
|
||||
break
|
||||
vals.append(t - lo)
|
||||
if ok:
|
||||
frames.append(vals)
|
||||
i += 7
|
||||
else:
|
||||
i += 1
|
||||
resyncs += 1
|
||||
self._last_resyncs = resyncs
|
||||
if not frames:
|
||||
return None, 0
|
||||
l1 = [f[0] for f in frames]
|
||||
l2, l3 = [], []
|
||||
for f in frames:
|
||||
l2.append(f[1]); l3.append(f[2]); l3.append(f[3])
|
||||
l2.append(f[4]); l3.append(f[5]); l3.append(f[6])
|
||||
tensors = [
|
||||
torch.tensor(l1)[None].to(self.device),
|
||||
torch.tensor(l2)[None].to(self.device),
|
||||
torch.tensor(l3)[None].to(self.device),
|
||||
]
|
||||
wav = self.snac.decode(tensors).squeeze().detach().cpu().numpy()
|
||||
return wav, len(frames)
|
||||
|
||||
def _wave_to_b64_raw(self, wav):
|
||||
"""Legacy parity: base64 of raw little-endian PCM buffer, no container."""
|
||||
return base64.b64encode(
|
||||
np.ascontiguousarray(wav.astype(self.RAW_DTYPE)).tobytes()
|
||||
).decode("utf-8")
|
||||
|
||||
# -------------------------------------------------------------- entry
|
||||
def __call__(self, data):
|
||||
try:
|
||||
target_text = data.get("inputs")
|
||||
if not target_text:
|
||||
return {"error": "Missing 'inputs' (target text)."}
|
||||
|
||||
p = data.get("parameters", {}) or {}
|
||||
task = str(p.get("task", "tts")).lower()
|
||||
mode = str(p.get("mode", "vanilla")).lower()
|
||||
method = str(p.get("method", "m2")).lower()
|
||||
if mode in ("translation", "trans"):
|
||||
mode = "translate"
|
||||
|
||||
if task not in ("tts", "vc"):
|
||||
return {"error": "parameters.task must be 'tts' or 'vc'."}
|
||||
if mode not in ("vanilla", "translate"):
|
||||
return {"error": "parameters.mode must be 'vanilla' or 'translate'."}
|
||||
if mode == "translate" and not p.get("language"):
|
||||
return {"error": "parameters.language is required for translate mode."}
|
||||
|
||||
gen_params = {
|
||||
"temperature": float(p.get("temperature", 0.6)),
|
||||
"top_p": float(p.get("top_p", 0.95)),
|
||||
"top_k": int(p.get("top_k", 50)),
|
||||
"max_new_tokens": int(p.get("max_new_tokens", 1200)),
|
||||
"repetition_penalty": float(p.get("repetition_penalty", 1.1)),
|
||||
}
|
||||
if not 0 < gen_params["top_p"] <= 1:
|
||||
return {"error": "top_p must be within (0, 1]."}
|
||||
if not (gen_params["top_k"] == -1 or gen_params["top_k"] > 0):
|
||||
return {"error": "top_k must be -1 (disabled) or a positive integer."}
|
||||
if not 0 < gen_params["repetition_penalty"] <= 2:
|
||||
return {"error": "repetition_penalty must be within (0, 2]."}
|
||||
if gen_params["max_new_tokens"] <= 0:
|
||||
return {"error": "max_new_tokens must be positive."}
|
||||
|
||||
if task == "vc":
|
||||
ref_text = p.get("reference_text")
|
||||
ref_audio = p.get("reference_audio")
|
||||
if not ref_text or not ref_audio:
|
||||
return {"error": "VC requires parameters.reference_text and "
|
||||
"parameters.reference_audio (base64)."}
|
||||
if method not in ("m1", "m2"):
|
||||
return {"error": "parameters.method must be 'm1' or 'm2'."}
|
||||
ref_wave = self._b64_to_wave(ref_audio)
|
||||
ref_codes = self._dedup_frames(self._audio_to_codes(ref_wave))
|
||||
if not ref_codes:
|
||||
return {"error": "Reference audio produced no SNAC codes."}
|
||||
prompt_ids = self.build_vc_prompt(
|
||||
ref_text, ref_codes, target_text, mode, p.get("language"), method)
|
||||
else:
|
||||
prompt_ids = self.build_tts_prompt(
|
||||
target_text, p.get("voice") or p.get("speaker"),
|
||||
mode, p.get("language"))
|
||||
|
||||
budget = self.MAX_MODEL_LEN - gen_params["max_new_tokens"]
|
||||
if len(prompt_ids) > budget:
|
||||
return {"error": f"Prompt is {len(prompt_ids)} tokens; exceeds budget "
|
||||
f"{budget} (max_model_len - max_new_tokens). "
|
||||
f"Shorten the reference clip or text."}
|
||||
|
||||
gen_ids = self._generate(prompt_ids, gen_params)
|
||||
wav, n_frames = self._codes_to_wave(gen_ids)
|
||||
if wav is None:
|
||||
return {"error": "Model generated no audio tokens.",
|
||||
"input_tokens": len(prompt_ids),
|
||||
"generated_tokens": len(gen_ids)}
|
||||
|
||||
return {
|
||||
"audio_b64": self._wave_to_b64_raw(wav), # RAW float32 PCM (legacy parity)
|
||||
"audio_dtype": np.dtype(self.RAW_DTYPE).name,
|
||||
"sample_rate": self.SNAC_SR,
|
||||
"duration_seconds": round(len(wav) / self.SNAC_SR, 3),
|
||||
"audio_frames": n_frames,
|
||||
"input_tokens": len(prompt_ids),
|
||||
"generated_tokens": len(gen_ids),
|
||||
"task": task, "mode": mode,
|
||||
"method": method if task == "vc" else None,
|
||||
"decode_resyncs": getattr(self, "_last_resyncs", 0),
|
||||
}
|
||||
|
||||
except ValueError as e:
|
||||
return {"error": str(e)}
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
return {"error": str(e)}
|
||||
3
model-00001-of-00002.safetensors
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3
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size 4991037968
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19
requirements.txt
Normal file
19
requirements.txt
Normal file
@@ -0,0 +1,19 @@
|
||||
--extra-index-url https://download.pytorch.org/whl/cu121
|
||||
transformers==4.51.3
|
||||
accelerate==1.6.0
|
||||
|
||||
vllm==0.8.5.post1
|
||||
torch==2.6.0
|
||||
torchaudio==2.6.0
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torchvision==0.21.0
|
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triton==3.2.0
|
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xformers==0.0.29.post2
|
||||
|
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numpy==1.26.4
|
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protobuf==4.25.3
|
||||
snac==1.2.1
|
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diffusers==0.30.3
|
||||
|
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starlette<1.0
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||||
librosa==0.10.2.post1
|
||||
soundfile==0.12.1
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fc3fecb199b4170636dbfab986d25f628157268d37b861f9cadaca60b1353bce
|
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size 22849547
|
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231538
tokenizer_config.json
Normal file
231538
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user