Замена модели ASR на GigaAM (CTC v2)

app.py

@@ -4,8 +4,8 @@ import subprocess
 import time
 from typing import Dict
 from typing import Optional, Union, List, Tuple
+import gigaam
 
-import whisper
 from fastapi import FastAPI, Depends, HTTPException, UploadFile, File
 from fastapi.security import APIKeyHeader
 
@@ -18,6 +18,7 @@ logger = logging.getLogger(__name__)
 
 app = FastAPI()
 
+model = gigaam.load_model("v2_ctc", device="cuda", download_root="./model")
 
 # API key header
 api_key_header = APIKeyHeader(name="x-api-key")
@@ -108,19 +109,7 @@ def get_audio_duration(file_path: str) -> float:
 async def transcribe_audio(
         file: UploadFile = File(...),
         token: str = Depends(api_key_header),
-        model_name: str = "turbo",
-        verbose: Optional[bool] = None,
-        temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
-        compression_ratio_threshold: Optional[float] = 2.4,
-        logprob_threshold: Optional[float] = -1.0,
-        no_speech_threshold: Optional[float] = 0.6,
-        condition_on_previous_text: bool = True,
-        initial_prompt: Optional[str] = None,
-        word_timestamps: bool = False,
-        prepend_punctuations: str = "\"'\"¿([{-",
-        append_punctuations: str = "\"\'.。,，!！?？:：\")]}、",
-        clip_timestamps: Union[str, List[float]] = "0",
-        hallucination_silence_threshold: Optional[float] = None
+        model_name: str = "turbo"
 ):
     # Token validation
     if token not in get_keys():
@@ -148,24 +137,29 @@ async def transcribe_audio(
 
         # Transcribe
         logger.info("Starting transcription")
-        result = model.transcribe(
-            temp_output_path,
-            verbose=verbose,
-            temperature=temperature,
-            compression_ratio_threshold=compression_ratio_threshold,
-            logprob_threshold=logprob_threshold,
-            no_speech_threshold=no_speech_threshold,
-            condition_on_previous_text=condition_on_previous_text,
-            initial_prompt=initial_prompt,
-            word_timestamps=word_timestamps,
-            prepend_punctuations=prepend_punctuations,
-            append_punctuations=append_punctuations,
-            clip_timestamps=clip_timestamps,
-            hallucination_silence_threshold=hallucination_silence_threshold
-        )
+        if original_duration > 30:
+            logger.info("Audio duration > 30 seconds, using transcribe_longform")
+            transcription_result = model.transcribe_longform(
+                temp_output_path
+            )
+        else:
+            logger.info("Audio duration <= 30 seconds, using transcribe")
+            transcription_result = model.transcribe(
+                temp_output_path
+            )
+
+        full_text = ""
+        for part in transcription_result:
+            if part["transcription"].strip() != "":
+                full_text += part["transcription"].strip() + " "
+
+        result = {
+            "transcription": transcription_result,
+            "text": full_text
+        }
 
         # Calculate metrics
-        metrics.stop(result["text"], original_duration)
+        metrics.stop(full_text, original_duration)
         logger.info(f"Transcription metrics: {metrics.get_metrics()}")
 
         # Add metrics to result