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import torch
import librosa
from transformers import (
AutoModelForSpeechSeq2Seq,
AutoProcessor,
pipeline,
Wav2Vec2ForCTC,
Wav2Vec2Processor
)
import warnings
import logging
import os
from typing import List, Dict, Optional, Union
import numpy as np
warnings.filterwarnings("ignore")
class FreeIndianSpeechToText:
"""
Complete Speech-to-Text implementation for Indian languages using free open-source models.
Supports multiple model architectures optimized for different use cases.
"""
def __init__(self, model_type: str = "distil-whisper", language: str = "hindi", cache_dir: str = "./models"):
self.language = language
self.cache_dir = cache_dir
self.device = "cuda" if torch.cuda.is_available() and os.getenv("ENABLE_GPU", "True") == "True" else "cpu"
self.torch_dtype = torch.float16 if self.device == "cuda" else torch.float32
# Configure logging
logging.basicConfig(level=getattr(logging, os.getenv("LOG_LEVEL", "INFO")))
self.logger = logging.getLogger(__name__)
# Free model configurations with performance metrics
self.model_configs = {
"distil-whisper": {
"model_id": "distil-whisper/distil-large-v3",
"type": "whisper",
"description": "6x faster than Whisper, 49% smaller, <1% WER difference",
"languages": 99,
"size": "769M"
},
"whisper-free": {
"model_id": "openai/whisper-large-v3",
"type": "whisper",
"description": "Best accuracy, supports 99 languages",
"languages": 99,
"size": "1550M"
},
"whisper-small": {
"model_id": "openai/whisper-small",
"type": "whisper",
"description": "Balanced performance, good for CPU",
"languages": 99,
"size": "244M"
},
"wav2vec2-hindi": {
"model_id": "ai4bharat/indicwav2vec-hindi",
"type": "wav2vec2",
"description": "Specialized for Hindi, AI4Bharat model",
"languages": 1,
"size": "300M"
},
"wav2vec2-improved": {
"model_id": "yash072/wav2vec2-large-XLSR-Hindi-YashR",
"type": "wav2vec2",
"description": "Improved Hindi model, 54% WER",
"languages": 1,
"size": "300M"
},
"wav2vec2-multilang": {
"model_id": "theainerd/Wav2Vec2-large-xlsr-hindi",
"type": "wav2vec2",
"description": "Multi-language Wav2Vec2 for Hindi",
"languages": 1,
"size": "300M"
},
"seamless": {
"model_id": "facebook/seamless-m4t-v2-large",
"type": "seamless",
"description": "Meta's unified model, 101 languages",
"languages": 101,
"size": "2.3B"
},
"speecht5": {
"model_id": "microsoft/speecht5_asr",
"type": "speecht5",
"description": "Microsoft's unified speech model",
"languages": 10,
"size": "200M"
}
}
self.load_model(model_type)
def load_model(self, model_type: str) -> None:
"""Load the specified model with TensorFlow optimization."""
if model_type not in self.model_configs:
raise ValueError(f"Model type '{model_type}' not supported. Available: {list(self.model_configs.keys())}")
config = self.model_configs[model_type]
self.model_id = config["model_id"]
self.model_type = config["type"]
self.current_model_name = model_type
self.logger.info(f"Loading {model_type} model: {self.model_id}")
self.logger.info(f"Description: {config['description']}")
try:
if self.model_type == "whisper":
self._load_whisper_model()
elif self.model_type == "wav2vec2":
self._load_wav2vec2_model()
elif self.model_type in ["seamless", "speecht5"]:
self._load_pipeline_model()
self.logger.info(f"Successfully loaded {model_type} on {self.device}")
except Exception as e:
self.logger.error(f"Error loading model {model_type}: {str(e)}")
raise
def _load_whisper_model(self) -> None:
"""Load Whisper-based models with optimization."""
self.pipe = pipeline(
"automatic-speech-recognition",
model=self.model_id,
dtype=self.torch_dtype,
device=self.device,
model_kwargs={"cache_dir": self.cache_dir, "use_safetensors": True},
return_timestamps=True
)
def _load_wav2vec2_model(self) -> None:
"""Load Wav2Vec2 models."""
self.model = Wav2Vec2ForCTC.from_pretrained(
self.model_id,
cache_dir=self.cache_dir
).to(self.device)
self.processor = Wav2Vec2Processor.from_pretrained(
self.model_id,
cache_dir=self.cache_dir
)
def _load_pipeline_model(self) -> None:
"""Load pipeline-based models."""
self.pipe = pipeline(
"automatic-speech-recognition",
model=self.model_id,
device=self.device,
model_kwargs={"cache_dir": self.cache_dir}
)
def transcribe(self, audio_input: Union[str, np.ndarray], language_code: str = "hi") -> Dict:
"""
Transcribe audio to text with detailed results.
Args:
audio_input: Path to audio file or numpy array
language_code: Language code (hi=Hindi, ta=Tamil, bn=Bengali, etc.)
Returns:
Dictionary with transcription results and metadata
"""
try:
start_time = torch.cuda.Event(enable_timing=True) if self.device == "cuda" else None
end_time = torch.cuda.Event(enable_timing=True) if self.device == "cuda" else None
if start_time:
start_time.record()
if self.model_type == "whisper":
result = self._transcribe_whisper(audio_input, language_code)
elif self.model_type == "wav2vec2":
result = self._transcribe_wav2vec2(audio_input)
else:
result = self._transcribe_pipeline(audio_input)
if end_time:
end_time.record()
torch.cuda.synchronize()
processing_time = start_time.elapsed_time(end_time) / 1000.0
else:
processing_time = 0.0
return {
"text": result,
"model": self.current_model_name,
"language": language_code,
"processing_time": processing_time,
"device": self.device,
"success": True
}
except Exception as e:
self.logger.error(f"Transcription error: {str(e)}")
return {
"text": "",
"error": str(e),
"model": self.current_model_name,
"success": False
}
def _transcribe_whisper(self, audio_input: Union[str, np.ndarray], language_code: str) -> str:
"""Transcribe using Whisper-based models."""
generate_kwargs = {}
if language_code != "en":
language_name = self._get_language_name(language_code)
generate_kwargs = {
"language": language_name,
"task": "transcribe"
}
result = self.pipe(audio_input, generate_kwargs=generate_kwargs)
# Handle different return formats
if isinstance(result, dict):
return result.get("text", "")
elif isinstance(result, list) and len(result) > 0:
return result[0].get("text", "")
else:
return str(result)
def _transcribe_wav2vec2(self, audio_input: Union[str, np.ndarray]) -> str:
"""Transcribe using Wav2Vec2 models."""
if isinstance(audio_input, str):
audio, sr = librosa.load(audio_input, sr=16000)
else:
audio = audio_input
input_values = self.processor(
audio,
return_tensors="pt",
sampling_rate=16000
).input_values.to(self.device)
with torch.no_grad():
logits = self.model(input_values).logits
prediction_ids = torch.argmax(logits, dim=-1)
transcription = self.processor.batch_decode(prediction_ids)[0]
return transcription
def _transcribe_pipeline(self, audio_input: Union[str, np.ndarray]) -> str:
"""Transcribe using pipeline models."""
result = self.pipe(audio_input)
if isinstance(result, dict):
return result.get("text", "")
else:
return str(result)
def batch_transcribe(self, audio_paths: List[str], language_code: str = "hi") -> List[Dict]:
"""Transcribe multiple audio files efficiently."""
results = []
self.logger.info(f"Starting batch transcription of {len(audio_paths)} files")
for i, audio_path in enumerate(audio_paths):
self.logger.info(f"Processing file {i+1}/{len(audio_paths)}: {audio_path}")
try:
result = self.transcribe(audio_path, language_code)
result["file"] = audio_path
results.append(result)
except Exception as e:
results.append({
"file": audio_path,
"error": str(e),
"success": False
})
return results
def get_model_info(self) -> Dict:
"""Get information about the current model."""
config = self.model_configs[self.current_model_name]
return {
"name": self.current_model_name,
"model_id": self.model_id,
"type": self.model_type,
"description": config["description"],
"languages_supported": config["languages"],
"model_size": config["size"],
"device": self.device,
"torch_dtype": str(self.torch_dtype)
}
def get_available_models(self) -> Dict:
"""Get list of all available models."""
return {name: config for name, config in self.model_configs.items()}
def switch_model(self, model_type: str) -> bool:
"""Switch to a different model."""
try:
self.load_model(model_type)
return True
except Exception as e:
self.logger.error(f"Failed to switch to model {model_type}: {e}")
return False
def _get_language_name(self, code: str) -> str:
"""Convert language code to language name for Whisper models."""
lang_map = {
"hi": "hindi",
"ta": "tamil",
"bn": "bengali",
"te": "telugu",
"mr": "marathi",
"gu": "gujarati",
"kn": "kannada",
"ml": "malayalam",
"pa": "punjabi",
"or": "odia",
"as": "assamese",
"ur": "urdu",
"en": "english"
}
return lang_map.get(code, "hindi")
def preprocess_audio(self, audio_path: str, target_sr: int = 16000) -> np.ndarray:
"""Preprocess audio file for optimal transcription."""
try:
# Load audio
audio, sr = librosa.load(audio_path, sr=target_sr)
# Normalize audio
audio = librosa.util.normalize(audio)
# Remove silence
audio, _ = librosa.effects.trim(audio, top_db=20)
return audio
except Exception as e:
self.logger.error(f"Audio preprocessing error: {e}")
raise
def get_supported_languages(self) -> List[str]:
"""Get list of supported Indian languages."""
return [
"hindi", "tamil", "bengali", "telugu", "marathi",
"gujarati", "kannada", "malayalam", "punjabi", "odia",
"assamese", "urdu", "english"
]
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