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import gradio as gr
import whisper
import torch
import os
from pydub import AudioSegment
from transformers import pipeline
from faster_whisper import WhisperModel # Import faster-whisper
# Mapping of model names to Whisper model sizes
MODELS = {
"Tiny (Fastest)": "tiny",
"Base (Faster)": "base",
"Small (Balanced)": "small",
"Medium (Accurate)": "medium",
"Large (Most Accurate)": "large",
"Systran Faster Whisper Large v3": "Systran/faster-whisper-large-v3" # Add the new model
}
# Fine-tuned models for specific languages
FINE_TUNED_MODELS = {
"Tamil": {
"model": "vasista22/whisper-tamil-medium",
"language": "ta"
},
# Add more fine-tuned models for other languages here
}
# Mapping of full language names to language codes
LANGUAGE_NAME_TO_CODE = {
"Auto Detect": "Auto Detect",
"English": "en",
"Chinese": "zh",
"German": "de",
"Spanish": "es",
"Russian": "ru",
"Korean": "ko",
"French": "fr",
"Japanese": "ja",
"Portuguese": "pt",
"Turkish": "tr",
"Polish": "pl",
"Catalan": "ca",
"Dutch": "nl",
"Arabic": "ar",
"Swedish": "sv",
"Italian": "it",
"Indonesian": "id",
"Hindi": "hi",
"Finnish": "fi",
"Vietnamese": "vi",
"Hebrew": "he",
"Ukrainian": "uk",
"Greek": "el",
"Malay": "ms",
"Czech": "cs",
"Romanian": "ro",
"Danish": "da",
"Hungarian": "hu",
"Tamil": "ta",
"Norwegian": "no",
"Thai": "th",
"Urdu": "ur",
"Croatian": "hr",
"Bulgarian": "bg",
"Lithuanian": "lt",
"Latin": "la",
"Maori": "mi",
"Malayalam": "ml",
"Welsh": "cy",
"Slovak": "sk",
"Telugu": "te",
"Persian": "fa",
"Latvian": "lv",
"Bengali": "bn",
"Serbian": "sr",
"Azerbaijani": "az",
"Slovenian": "sl",
"Kannada": "kn",
"Estonian": "et",
"Macedonian": "mk",
"Breton": "br",
"Basque": "eu",
"Icelandic": "is",
"Armenian": "hy",
"Nepali": "ne",
"Mongolian": "mn",
"Bosnian": "bs",
"Kazakh": "kk",
"Albanian": "sq",
"Swahili": "sw",
"Galician": "gl",
"Marathi": "mr",
"Punjabi": "pa",
"Sinhala": "si", # Sinhala support
"Khmer": "km",
"Shona": "sn",
"Yoruba": "yo",
"Somali": "so",
"Afrikaans": "af",
"Occitan": "oc",
"Georgian": "ka",
"Belarusian": "be",
"Tajik": "tg",
"Sindhi": "sd",
"Gujarati": "gu",
"Amharic": "am",
"Yiddish": "yi",
"Lao": "lo",
"Uzbek": "uz",
"Faroese": "fo",
"Haitian Creole": "ht",
"Pashto": "ps",
"Turkmen": "tk",
"Nynorsk": "nn",
"Maltese": "mt",
"Sanskrit": "sa",
"Luxembourgish": "lb",
"Burmese": "my",
"Tibetan": "bo",
"Tagalog": "tl",
"Malagasy": "mg",
"Assamese": "as",
"Tatar": "tt",
"Hawaiian": "haw",
"Lingala": "ln",
"Hausa": "ha",
"Bashkir": "ba",
"Javanese": "jw",
"Sundanese": "su",
}
# Reverse mapping of language codes to full language names
CODE_TO_LANGUAGE_NAME = {v: k for k, v in LANGUAGE_NAME_TO_CODE.items()}
# Device and compute type for faster-whisper
device, torch_dtype = ("cuda", "float32") if torch.cuda.is_available() else ("cpu", "int8")
def detect_language(audio_file):
"""Detect the language of the audio file."""
# Load the Whisper model (use "base" for faster detection)
model = whisper.load_model("base")
# Convert audio to 16kHz mono for better compatibility with Whisper
audio = AudioSegment.from_file(audio_file)
audio = audio.set_frame_rate(16000).set_channels(1)
processed_audio_path = "processed_audio.wav"
audio.export(processed_audio_path, format="wav")
# Detect the language
result = model.transcribe(processed_audio_path, task="detect_language", fp16=False)
detected_language_code = result.get("language", "unknown")
# Get the full language name from the code
detected_language = CODE_TO_LANGUAGE_NAME.get(detected_language_code, "Unknown Language")
# Clean up processed audio file
os.remove(processed_audio_path)
return f"Detected Language: {detected_language}"
def transcribe_audio(audio_file, language="Auto Detect", model_size="Base (Faster)"):
"""Transcribe the audio file."""
# Convert audio to 16kHz mono for better compatibility
audio = AudioSegment.from_file(audio_file)
audio = audio.set_frame_rate(16000).set_channels(1)
processed_audio_path = "processed_audio.wav"
audio.export(processed_audio_path, format="wav")
# Load the appropriate model
if language in FINE_TUNED_MODELS:
# Use the fine-tuned Whisper model for the selected language
device = "cuda:0" if torch.cuda.is_available() else "cpu"
transcribe = pipeline(
task="automatic-speech-recognition",
model=FINE_TUNED_MODELS[language]["model"],
chunk_length_s=30,
device=device
)
transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(
language=FINE_TUNED_MODELS[language]["language"],
task="transcribe"
)
result = transcribe(processed_audio_path)
transcription = result["text"]
detected_language = language
else:
# Use the selected Whisper model
if model_size == "Systran Faster Whisper Large v3":
# Use faster-whisper for the Systran model
model = WhisperModel(MODELS[model_size], device=device, compute_type=torch_dtype)
segments, info = model.transcribe(
processed_audio_path,
task="transcribe",
word_timestamps=True,
repetition_penalty=1.1,
temperature=[0.0, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0],
)
transcription = " ".join([segment.text for segment in segments])
detected_language_code = info.language
detected_language = CODE_TO_LANGUAGE_NAME.get(detected_language_code, "Unknown Language")
else:
# Use the standard Whisper model
model = whisper.load_model(MODELS[model_size])
# Transcribe the audio
if language == "Auto Detect":
result = model.transcribe(processed_audio_path, fp16=False) # Auto-detect language
detected_language_code = result.get("language", "unknown")
detected_language = CODE_TO_LANGUAGE_NAME.get(detected_language_code, "Unknown Language")
else:
language_code = LANGUAGE_NAME_TO_CODE.get(language, "en") # Default to English if not found
result = model.transcribe(processed_audio_path, language=language_code, fp16=False)
detected_language = language
transcription = result["text"]
# Clean up processed audio file
os.remove(processed_audio_path)
# Return transcription and detected language
return f"Detected Language: {detected_language}\n\nTranscription:\n{transcription}"
# Define the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Audio Transcription and Language Detection")
with gr.Tab("Detect Language"):
gr.Markdown("Upload an audio file to detect its language.")
detect_audio_input = gr.Audio(type="filepath", label="Upload Audio File")
detect_language_output = gr.Textbox(label="Detected Language")
detect_button = gr.Button("Detect Language")
with gr.Tab("Transcribe Audio"):
gr.Markdown("Upload an audio file, select a language (or choose 'Auto Detect'), and choose a model for transcription.")
transcribe_audio_input = gr.Audio(type="filepath", label="Upload Audio File")
language_dropdown = gr.Dropdown(
choices=list(LANGUAGE_NAME_TO_CODE.keys()), # Full language names
label="Select Language",
value="Auto Detect"
)
model_dropdown = gr.Dropdown(
choices=list(MODELS.keys()), # Model options
label="Select Model",
value="Base (Faster)", # Default to "Base" model
interactive=True # Allow model selection by default
)
transcribe_output = gr.Textbox(label="Transcription and Detected Language")
transcribe_button = gr.Button("Transcribe Audio")
# Link buttons to functions
detect_button.click(detect_language, inputs=detect_audio_input, outputs=detect_language_output)
transcribe_button.click(transcribe_audio, inputs=[transcribe_audio_input, language_dropdown, model_dropdown], outputs=transcribe_output)
# Launch the Gradio interface
demo.launch() |