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"""Speech-to-Text service using Gradio Client API."""
import asyncio
import tempfile
from functools import lru_cache
from pathlib import Path
from typing import Any
import numpy as np
import structlog
from gradio_client import Client, handle_file
from src.utils.config import settings
from src.utils.exceptions import ConfigurationError
logger = structlog.get_logger(__name__)
class STTService:
"""STT service using nvidia/canary-1b-v2 Gradio Space."""
def __init__(self, api_url: str | None = None, hf_token: str | None = None) -> None:
"""Initialize STT service.
Args:
api_url: Gradio Space URL (default: settings.stt_api_url or nvidia/canary-1b-v2)
hf_token: HuggingFace token for authenticated Spaces (default: None)
Raises:
ConfigurationError: If API URL not configured
"""
self.api_url = api_url or settings.stt_api_url or "https://nvidia-canary-1b-v2.hf.space"
if not self.api_url:
raise ConfigurationError("STT API URL not configured")
self.hf_token = hf_token
self.client: Client | None = None
async def _get_client(self, hf_token: str | None = None) -> Client:
"""Get or create Gradio Client (lazy initialization).
Args:
hf_token: HuggingFace token for authenticated Spaces (overrides instance token)
Returns:
Gradio Client instance
"""
# Use provided token or instance token
token = hf_token or self.hf_token
# If client exists but token changed, recreate it
if self.client is not None and token != self.hf_token:
self.client = None
if self.client is None:
loop = asyncio.get_running_loop()
# Pass token to Client for authenticated Spaces
# Gradio Client uses 'token' parameter, not 'hf_token'
if token:
self.client = await loop.run_in_executor(
None,
lambda: Client(self.api_url, token=token),
)
else:
self.client = await loop.run_in_executor(
None,
lambda: Client(self.api_url),
)
# Update instance token for future use
self.hf_token = token
return self.client
async def transcribe_file(
self,
audio_path: str,
source_lang: str | None = None,
target_lang: str | None = None,
hf_token: str | None = None,
) -> str:
"""Transcribe audio file using Gradio API.
Args:
audio_path: Path to audio file
source_lang: Source language (default: settings.stt_source_lang)
target_lang: Target language (default: settings.stt_target_lang)
Returns:
Transcribed text string
Raises:
ConfigurationError: If transcription fails
"""
client = await self._get_client(hf_token=hf_token)
source_lang = source_lang or settings.stt_source_lang
target_lang = target_lang or settings.stt_target_lang
logger.info(
"transcribing_audio_file",
audio_path=audio_path,
source_lang=source_lang,
target_lang=target_lang,
)
try:
# Call /transcribe_file API endpoint
# API returns: (dataframe, csv_path, srt_path)
loop = asyncio.get_running_loop()
result = await loop.run_in_executor(
None,
lambda: client.predict(
audio_path=handle_file(audio_path),
source_lang=source_lang,
target_lang=target_lang,
api_name="/transcribe_file",
),
)
# Extract transcription from result
transcribed_text = self._extract_transcription(result)
logger.info(
"audio_transcription_complete",
text_length=len(transcribed_text),
)
return transcribed_text
except Exception as e:
logger.error("audio_transcription_failed", error=str(e), error_type=type(e).__name__)
raise ConfigurationError(f"Audio transcription failed: {e}") from e
async def transcribe_audio(
self,
audio_data: tuple[int, np.ndarray[Any, Any]], # type: ignore[type-arg]
hf_token: str | None = None,
) -> str:
"""Transcribe audio numpy array to text.
Args:
audio_data: Tuple of (sample_rate, audio_array)
Returns:
Transcribed text string
"""
sample_rate, audio_array = audio_data
logger.info(
"transcribing_audio_array",
sample_rate=sample_rate,
audio_shape=audio_array.shape,
)
# Save audio to temp file
temp_path = self._save_audio_temp(audio_data)
try:
# Transcribe the temp file
transcribed_text = await self.transcribe_file(temp_path, hf_token=hf_token)
return transcribed_text
finally:
# Clean up temp file
try:
Path(temp_path).unlink(missing_ok=True)
except Exception as e:
logger.warning("failed_to_cleanup_temp_file", path=temp_path, error=str(e))
def _extract_transcription(self, api_result: tuple[Any, ...]) -> str:
"""Extract transcription text from API result.
Args:
api_result: Tuple from Gradio API (dataframe, csv_path, srt_path)
Returns:
Extracted transcription text
"""
# API returns: (dataframe, csv_path, srt_path)
# Try to extract from dataframe first
if isinstance(api_result, tuple) and len(api_result) >= 1:
dataframe = api_result[0]
if isinstance(dataframe, dict) and "data" in dataframe:
# Extract text from dataframe rows
rows = dataframe.get("data", [])
if rows:
# Combine all text segments
text_segments = []
for row in rows:
if isinstance(row, list) and len(row) > 0:
# First column is usually the text
text_segments.append(str(row[0]))
if text_segments:
return " ".join(text_segments)
# Fallback: try to read CSV file if available
if len(api_result) >= 2 and api_result[1]:
csv_path = api_result[1]
try:
import pandas as pd
df = pd.read_csv(csv_path)
if "text" in df.columns:
return " ".join(df["text"].astype(str).tolist())
elif len(df.columns) > 0:
# Use first column
return " ".join(df.iloc[:, 0].astype(str).tolist())
except Exception as e:
logger.warning("failed_to_read_csv", csv_path=csv_path, error=str(e))
# Last resort: return empty string
logger.warning("could_not_extract_transcription", result_type=type(api_result).__name__)
return ""
def _save_audio_temp(
self,
audio_data: tuple[int, np.ndarray[Any, Any]], # type: ignore[type-arg]
) -> str:
"""Save audio numpy array to temporary WAV file.
Args:
audio_data: Tuple of (sample_rate, audio_array)
Returns:
Path to temporary WAV file
"""
sample_rate, audio_array = audio_data
# Create temp file
temp_file = tempfile.NamedTemporaryFile(
suffix=".wav",
delete=False,
)
temp_path = temp_file.name
temp_file.close()
# Save audio using soundfile
try:
import soundfile as sf
# Ensure audio is float32 and mono
if audio_array.dtype != np.float32:
audio_array = audio_array.astype(np.float32)
# Handle stereo -> mono conversion
if len(audio_array.shape) > 1:
audio_array = np.mean(audio_array, axis=1)
# Normalize to [-1, 1] range
if audio_array.max() > 1.0 or audio_array.min() < -1.0:
audio_array = audio_array / np.max(np.abs(audio_array))
sf.write(temp_path, audio_array, sample_rate)
logger.debug("saved_audio_temp", path=temp_path, sample_rate=sample_rate)
return temp_path
except ImportError:
raise ConfigurationError(
"soundfile not installed. Install with: uv add soundfile"
) from None
except Exception as e:
logger.error("failed_to_save_audio_temp", error=str(e))
raise ConfigurationError(f"Failed to save audio to temp file: {e}") from e
@lru_cache(maxsize=1)
def get_stt_service() -> STTService:
"""Get or create singleton STT service instance.
Returns:
STTService instance
"""
return STTService()
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