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Model: hypaai/Hypa-Orpheus-Step-latest-16bit
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+base_model: unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded finetuned  model
+
+- **Developed by:** hypaai
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
+
+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)

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "torch_dtype": "bfloat16",
+    "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_parameters": {
+        "factor": 32.0,
+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_theta": 500000.0,
+        "rope_type": "llama3"
+    },
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.7.3",
+    "use_cache": true,
+    "vocab_size": 156940
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128009,
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.13.1"
+}

handler.py

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+"""
+HF Inference Endpoint handler — Hypa Orpheus TTS + Voice Cloning (merged 16-bit).
+
+Task matrix (routed by `parameters`):
+  task="tts", mode="vanilla"    : {speaker}: text                      -> speech
+  task="tts", mode="translate"  : {speaker} - {Language}: text         -> speech in Language
+  task="vc",  mode="vanilla"    : reference (text+audio) + target text -> speech in reference voice
+  task="vc",  mode="translate"  : + language tag on target text        -> cross-lingual cloning
+  VC method="m1" (in-context) | method="m2" (continue-speaking)
+
+Output parity with the legacy endpoint: `audio_b64` is base64 of the RAW
+float32 little-endian mono PCM buffer at 24000 Hz (NO WAV/RIFF container),
+so existing products decode with:  np.frombuffer(base64.b64decode(s), dtype=np.float32)
+[If the legacy endpoint used int16, change RAW_DTYPE to np.int16 below.]
+
+Prompts are byte-identical to Step-III training (_encode_text / build_tts /
+build_vc_both), reference codes are frame-deduped, and prompts reach vLLM as
+token ids (never a decoded string).
+"""
+
+import io
+import os
+import base64
+import tempfile
+import traceback
+
+import numpy as np
+import torch
+import soundfile as sf
+import librosa
+
+from transformers import AutoTokenizer
+from snac import SNAC
+from vllm import LLM, SamplingParams
+
+
+class EndpointHandler:
+    # ---- Orpheus special tokens (fixed by the model) ----
+    TOKENISER_LEN   = 128256
+    START_OF_TEXT   = 128000
+    END_OF_TEXT     = 128009
+    START_OF_SPEECH = TOKENISER_LEN + 1   # 128257
+    END_OF_SPEECH   = TOKENISER_LEN + 2   # 128258
+    START_OF_HUMAN  = TOKENISER_LEN + 3   # 128259
+    END_OF_HUMAN    = TOKENISER_LEN + 4   # 128260
+    START_OF_AI     = TOKENISER_LEN + 5   # 128261
+    END_OF_AI       = TOKENISER_LEN + 6   # 128262
+    AUDIO_OFFSET    = 128266
+
+    # NOTE: fine-tune data capped at 2048 tokens; 4096 kept so M1-VC prompts
+    # (ref codes + two texts, often 1000-2000 tokens) retain a generation
+    # budget. Base Llama-3 RoPE supports these positions natively; expect the
+    # best quality when prompt+generation stays near the trained ~2048.
+    MAX_MODEL_LEN   = 4096
+    MAX_REF_SECONDS = 30
+    SNAC_SR         = 24000
+    RAW_DTYPE       = np.float32          # legacy raw-PCM dtype (see docstring)
+
+    LANG_DISPLAY = {
+        "en": "English", "es": "Spanish", "fr": "French", "ha": "Hausa",
+        "yo": "Yoruba", "sw": "Swahili", "ar": "Arabic", "pt": "Portuguese",
+        "ann": "Annang", "ebi": "Ebira", "efi": "Efik", "ego": "Eggon",
+        "urh": "Urhobo", "ibb": "Ibibio", "idm": "Idoma", "igl": "Igala",
+        "ig": "Igbo", "nup": "Nupe", "tiv": "Tiv", "pg": "Pidgin",
+    }
+
+    # ------------------------------------------------------------------ init
+    def __init__(self, path=""):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        # SNAC first (tiny, ~80 MB) so it never contends with vLLM's reservation.
+        self.snac = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to(self.device).eval()
+        self.model = LLM(
+            path,
+            max_model_len=self.MAX_MODEL_LEN,
+            gpu_memory_utilization=0.75,
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+
+    # ------------------------------------------------------- text encoding
+    def _lang_display(self, x):
+        if x is None:
+            return None
+        k = str(x).strip().lower()
+        return self.LANG_DISPLAY.get(k, k.capitalize() if k else None)
+
+    def _encode_text(self, text, speaker=None, lang_tag=None, add_bos=True):
+        text = "" if text is None else str(text).strip()
+        spk = speaker if (speaker and str(speaker).strip().lower() not in ("", "random", "none")) else None
+        if spk and lang_tag:
+            prompt = f"{spk} - {lang_tag}: {text}"
+        elif spk:
+            prompt = f"{spk}: {text}"
+        elif lang_tag:
+            prompt = f"{lang_tag}: {text}"
+        else:
+            prompt = text
+        ids = self.tokenizer.encode(prompt, add_special_tokens=add_bos)
+        ids.append(self.END_OF_TEXT)
+        return ids
+
+    # ------------------------------------------------------ audio encoding
+    def _b64_to_wave(self, b64_str):
+        raw = base64.b64decode(b64_str)
+        if not raw:
+            raise ValueError("reference_audio is empty.")
+        try:
+            arr, sr = sf.read(io.BytesIO(raw), dtype="float32")
+        except Exception:
+            # temp-file fallback: librosa/audioread handles containers
+            # libsndfile can't open, but needs a real file path for some codecs.
+            tmp = None
+            try:
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".audio") as f:
+                    f.write(raw)
+                    tmp = f.name
+                arr, sr = librosa.load(tmp, sr=None, mono=False)
+                arr = np.asarray(arr, dtype=np.float32)
+                if arr.ndim > 1:
+                    arr = arr.T                    # librosa returns (ch, n)
+            finally:
+                if tmp and os.path.exists(tmp):
+                    os.remove(tmp)
+        if arr.ndim > 1:
+            arr = arr.mean(axis=1)
+        if arr.size == 0 or not np.isfinite(arr).all():
+            raise ValueError("Reference audio is empty or contains invalid samples.")
+        if sr != self.SNAC_SR:
+            arr = librosa.resample(arr.astype(np.float32), orig_sr=sr, target_sr=self.SNAC_SR)
+        dur = len(arr) / self.SNAC_SR
+        if dur > self.MAX_REF_SECONDS:
+            raise ValueError(f"Reference audio is {dur:.1f}s; max is {self.MAX_REF_SECONDS}s. "
+                             f"Send a shorter clip.")
+        return arr.astype(np.float32)
+
+    @torch.inference_mode()
+    def _audio_to_codes(self, arr):
+        wav = torch.from_numpy(arr).to(self.device)[None, None]
+        codes = self.snac.encode(wav)
+        c0, c1, c2 = codes[0][0].tolist(), codes[1][0].tolist(), codes[2][0].tolist()
+        n = min(len(c0), len(c1) // 2, len(c2) // 4)
+        out = []
+        for i in range(n):
+            out += [
+                c0[i]         + self.AUDIO_OFFSET,
+                c1[2 * i]     + self.AUDIO_OFFSET + 4096,
+                c2[4 * i]     + self.AUDIO_OFFSET + 2 * 4096,
+                c2[4 * i + 1] + self.AUDIO_OFFSET + 3 * 4096,
+                c1[2 * i + 1] + self.AUDIO_OFFSET + 4 * 4096,
+                c2[4 * i + 2] + self.AUDIO_OFFSET + 5 * 4096,
+                c2[4 * i + 3] + self.AUDIO_OFFSET + 6 * 4096,
+            ]
+        return out
+
+    @staticmethod
+    def _dedup_frames(codes):
+        if not codes:
+            return codes
+        codes = codes[: (len(codes) // 7) * 7]
+        if len(codes) < 7:
+            return codes
+        result = codes[:7]
+        for i in range(7, len(codes), 7):
+            if codes[i] != result[-7]:
+                result.extend(codes[i:i + 7])
+        return result
+
+    # ------------------------------------------------------ prompt builders
+    def build_tts_prompt(self, text, speaker, mode, language):
+        lang_tag = self._lang_display(language) if mode == "translate" else None
+        tt = self._encode_text(text, speaker, lang_tag, add_bos=True)
+        return [self.START_OF_HUMAN] + tt + [self.END_OF_HUMAN,
+                self.START_OF_AI, self.START_OF_SPEECH]
+
+    def build_vc_prompt(self, ref_text, ref_codes, target_text, mode, language, method):
+        tag2 = self._lang_display(language) if mode == "translate" else None
+        tt1 = self._encode_text(ref_text, None, None, add_bos=True)
+        tt2 = self._encode_text(target_text, None, tag2, add_bos=False)
+        if method == "m1":
+            return ([self.START_OF_HUMAN] + tt1 + [self.END_OF_HUMAN,
+                     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),
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+                "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)}

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requirements.txt

@@ -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
+torchvision==0.21.0
+triton==3.2.0
+xformers==0.0.29.post2
+
+numpy==1.26.4
+protobuf==4.25.3
+snac==1.2.1
+diffusers==0.30.3
+
+starlette<1.0
+librosa==0.10.2.post1
+soundfile==0.12.1

tokenizer.json

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