File size: 3,874 Bytes
c7d2a58 c5098bd c7d2a58 029e0bd c7d2a58 029e0bd c7d2a58 0af2224 c7d2a58 029e0bd c7d2a58 029e0bd c7d2a58 029e0bd c7d2a58 029e0bd c7d2a58 029e0bd c7d2a58 029e0bd c7d2a58 c859499 c5098bd c7d2a58 029e0bd c7d2a58 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
import logging
import torch
import os
import base64
from pyannote.audio import Pipeline
from transformers import pipeline, AutoModelForCausalLM
from diarization_utils import diarize
from huggingface_hub import HfApi
from pydantic import ValidationError
from starlette.exceptions import HTTPException
from config import model_settings, InferenceConfig
logger = logging.getLogger(__name__)
class EndpointHandler():
def __init__(self, path=""):
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
logger.info(f"Using device: {device.type}")
torch_dtype = torch.float32 if device.type == "cpu" else torch.float16
self.assistant_model = AutoModelForCausalLM.from_pretrained(
model_settings.assistant_model,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True,
use_safetensors=True
) if model_settings.assistant_model else None
if self.assistant_model:
self.assistant_model.to(device)
self.asr_pipeline = pipeline(
"automatic-speech-recognition",
model=model_settings.asr_model,
torch_dtype=torch_dtype,
device=device
)
if model_settings.diarization_model:
# diarization pipeline doesn't raise if there is no token
HfApi().whoami(model_settings.hf_token)
self.diarization_pipeline = Pipeline.from_pretrained(
checkpoint_path=model_settings.diarization_model,
use_auth_token=model_settings.hf_token,
)
self.diarization_pipeline.to(device)
else:
self.diarization_pipeline = None
def __call__(self, inputs):
file = inputs.pop("inputs")
file = base64.b64decode(file)
parameters = inputs.pop("parameters", {})
try:
parameters = InferenceConfig(**parameters)
except ValidationError as e:
logger.error(f"Error validating parameters: {e}")
raise HTTPException(status_code=400, detail=f"Error validating parameters: {e}")
logger.info(f"inference parameters: {parameters}")
generate_kwargs = {
"task": parameters.task,
"language": parameters.language,
"assistant_model": self.assistant_model if parameters.assisted else None
}
try:
asr_outputs = self.asr_pipeline(
file,
chunk_length_s=parameters.chunk_length_s,
batch_size=parameters.batch_size,
generate_kwargs=generate_kwargs,
return_timestamps=True,
)
except RuntimeError as e:
logger.error(f"ASR inference error: {str(e)}")
raise HTTPException(status_code=400, detail=f"ASR inference error: {str(e)}")
except Exception as e:
logger.error(f"Unknown error diring ASR inference: {str(e)}")
raise HTTPException(status_code=500, detail=f"Unknown error diring ASR inference: {str(e)}")
if self.diarization_pipeline:
try:
transcript = diarize(self.diarization_pipeline, file, parameters, asr_outputs)
except RuntimeError as e:
logger.error(f"Diarization inference error: {str(e)}")
raise HTTPException(status_code=400, detail=f"Diarization inference error: {str(e)}")
except Exception as e:
logger.error(f"Unknown error during diarization: {str(e)}")
raise HTTPException(status_code=500, detail=f"Unknown error during diarization: {str(e)}")
else:
transcript = []
return {
"speakers": transcript,
"chunks": asr_outputs["chunks"],
"text": asr_outputs["text"],
} |