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Custom Inference Handler for StutteredSpeechASR Model
Handles audio input and returns transcriptions for stuttered speech.
"""
import torch
import librosa
import numpy as np
import base64
import io
import logging
from copy import deepcopy
from typing import Dict, Any
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class EndpointHandler:
"""
Custom handler for StutteredSpeechASR inference endpoint.
This handler processes audio inputs and returns transcriptions
using the fine-tuned Whisper model for stuttered Mandarin speech.
"""
def __init__(self, path: str = ""):
"""
Initialize the handler by loading the model and processor.
Args:
path: Path to the model directory (provided by Inference Endpoints)
"""
logger.info("Initializing StutteredSpeechASR handler...")
# Determine device and dtype
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
logger.info(f"Using device: {self.device}")
logger.info(f"Using dtype: {self.torch_dtype}")
# Load model and processor
try:
self.model = AutoModelForSpeechSeq2Seq.from_pretrained(
path,
torch_dtype=self.torch_dtype
)
self.processor = AutoProcessor.from_pretrained(path)
self.model.to(self.device)
self.model.eval() # Set to evaluation mode
logger.info("Model and processor loaded successfully!")
except Exception as e:
logger.error(f"Error loading model: {e}")
raise
def _load_audio_from_bytes(self, audio_bytes: bytes) -> np.ndarray:
"""
Load audio from bytes and resample to 16kHz.
Args:
audio_bytes: Raw audio bytes
Returns:
Audio waveform as numpy array
"""
try:
# Load audio from bytes using librosa
audio_buffer = io.BytesIO(audio_bytes)
waveform, _ = librosa.load(audio_buffer, sr=16000, mono=True)
return waveform
except Exception as e:
logger.error(f"Error loading audio from bytes: {e}")
raise ValueError(f"Failed to load audio: {e}")
def _load_audio_from_base64(self, base64_string: str) -> np.ndarray:
"""
Load audio from base64-encoded string.
Args:
base64_string: Base64-encoded audio data
Returns:
Audio waveform as numpy array
"""
try:
# Decode base64 string
audio_bytes = base64.b64decode(base64_string)
return self._load_audio_from_bytes(audio_bytes)
except Exception as e:
logger.error(f"Error decoding base64 audio: {e}")
raise ValueError(f"Failed to decode base64 audio: {e}")
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""
Process incoming requests and return transcriptions.
Expected input formats:
1. {"inputs": "base64_encoded_audio_string"}
2. {"inputs": {"audio": "base64_encoded_audio_string"}}
3. Binary audio data in request body
Dictionary requests may include {"parameters": {"language": "en"}}.
Omit language (or use null) to keep the model's default behavior.
Args:
data: Input data dictionary
Returns:
Dictionary containing transcription and metadata
"""
try:
logger.info("Processing inference request...")
generate_kwargs = {}
if isinstance(data, dict):
parameters = data.get("parameters", {})
if not isinstance(parameters, dict):
raise ValueError("'parameters' must be a dictionary")
language = parameters.get("language")
if language is not None:
if not isinstance(language, str) or not language.strip():
raise ValueError("'language' must be a non-empty string or null")
# Keep language overrides local to this request, including on
# Transformers versions that modify the generation config.
generate_kwargs = {
"generation_config": deepcopy(self.model.generation_config),
"language": language.strip(),
"task": "transcribe",
}
# Extract audio data from various input formats
waveform = None
if isinstance(data, dict):
# Format 1: {"inputs": "base64_string"}
if "inputs" in data:
inputs = data["inputs"]
if isinstance(inputs, str):
# Base64-encoded audio
waveform = self._load_audio_from_base64(inputs)
elif isinstance(inputs, dict):
# Format 2: {"inputs": {"audio": "base64_string"}}
if "audio" in inputs:
waveform = self._load_audio_from_base64(inputs["audio"])
else:
raise ValueError("Missing 'audio' field in inputs dictionary")
elif isinstance(inputs, bytes):
# Binary audio data
waveform = self._load_audio_from_bytes(inputs)
else:
raise ValueError(f"Unsupported input type: {type(inputs)}")
# Direct audio field
elif "audio" in data:
audio_data = data["audio"]
if isinstance(audio_data, str):
waveform = self._load_audio_from_base64(audio_data)
elif isinstance(audio_data, bytes):
waveform = self._load_audio_from_bytes(audio_data)
else:
raise ValueError("No valid audio data found in request. Expected 'inputs' or 'audio' field.")
elif isinstance(data, (bytes, bytearray)):
# Format 3: Direct binary data
waveform = self._load_audio_from_bytes(bytes(data))
else:
raise ValueError(f"Unsupported data type: {type(data)}")
if waveform is None:
raise ValueError("Failed to extract audio from request")
logger.info(f"Audio loaded: {len(waveform)} samples at 16kHz")
# Process audio with the processor
input_features = self.processor(
waveform,
sampling_rate=16000,
return_tensors="pt"
).input_features
# Move to device
input_features = input_features.to(self.device, dtype=self.torch_dtype)
# Use the requested language, or the model's defaults when omitted.
with torch.no_grad():
predicted_ids = self.model.generate(input_features, **generate_kwargs)
# Decode transcription
transcription = self.processor.batch_decode(
predicted_ids,
skip_special_tokens=True
)[0]
logger.info(f"Transcription complete: {transcription[:100]}...")
# Return result
return {
"transcription": transcription.strip(),
"audio_duration_seconds": float(len(waveform) / 16000),
"model": "AImpower/StutteredSpeechASR"
}
except Exception as e:
logger.error(f"Error during inference: {e}", exc_info=True)
return {
"error": str(e),
"transcription": None
}
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