Update realtime_Lora.py

realtime_Lora.py

@@ -0,0 +1,135 @@
+import io
+import os
+import torch
+from transformers import (
+    AutomaticSpeechRecognitionPipeline,
+    WhisperForConditionalGeneration,
+    WhisperTokenizer,
+    WhisperProcessor,
+)
+from peft import PeftModel, PeftConfig
+import speech_recognition as sr
+from datetime import datetime, timedelta
+from queue import Queue
+from tempfile import NamedTemporaryFile
+from time import sleep
+from sys import platform
+
+
+
+def main():
+    # Set your default configuration values here
+    peft_model_id = "DuyTa/Vietnamese_ASR"
+    language = "Vietnamese"
+    task = "transcribe"
+    default_energy_threshold = 900
+    default_record_timeout = 0.6
+    default_phrase_timeout = 3
+
+    # The last time a recording was retrieved from the queue.
+    phrase_time = None
+    # Current raw audio bytes.
+    last_sample = bytes()
+    # Thread safe Queue for passing data from the threaded recording callback.
+    data_queue = Queue()
+    # We use SpeechRecognizer to record our audio because it has a nice feature where it can detect when speech ends.
+    recorder = sr.Recognizer()
+    recorder.energy_threshold = default_energy_threshold
+    # Definitely do this, dynamic energy compensation lowers the energy threshold dramatically to a point where the SpeechRecognizer never stops recording.
+    recorder.dynamic_energy_threshold = False
+    
+    source = sr.Microphone(sample_rate=16000)  # Use default microphone source for non-Linux platforms
+
+    # Load / Download model
+    peft_config = PeftConfig.from_pretrained(peft_model_id)
+    model = WhisperForConditionalGeneration.from_pretrained(
+        peft_config.base_model_name_or_path
+    )
+    model = PeftModel.from_pretrained(model, peft_model_id)
+
+    model.to("cuda:0")
+    pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, batch_size=8, torch_dtype=torch.float32, device="cuda:0")
+
+    processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
+    
+
+    record_timeout = default_record_timeout
+    phrase_timeout = default_phrase_timeout
+
+    temp_file = NamedTemporaryFile().name
+    transcription = ['']
+
+    with source:
+        recorder.adjust_for_ambient_noise(source)
+
+    def record_callback(_, audio:sr.AudioData) -> None:
+        """
+        Threaded callback function to receive audio data when recordings finish.
+        audio: An AudioData containing the recorded bytes.
+        """
+        # Grab the raw bytes and push it into the thread safe queue.
+        data = audio.get_raw_data()
+        data_queue.put(data)
+
+    # Create a background thread that will pass us raw audio bytes.
+    # We could do this manually but SpeechRecognizer provides a nice helper.
+    recorder.listen_in_background(source, record_callback, phrase_time_limit=record_timeout)
+
+    print("Model loaded.\n")
+
+    while True:
+        try:
+            now = datetime.utcnow()
+            # Pull raw recorded audio from the queue.
+            if not data_queue.empty():
+                phrase_complete = False
+                # If enough time has passed between recordings, consider the phrase complete.
+                # Clear the current working audio buffer to start over with the new data.
+                if phrase_time and now - phrase_time > timedelta(seconds=phrase_timeout):
+                    last_sample = bytes()
+                    phrase_complete = True
+                # This is the last time we received new audio data from the queue.
+                phrase_time = now
+
+                # Concatenate our current audio data with the latest audio data.
+                while not data_queue.empty():
+                    data = data_queue.get()
+                    last_sample += data
+
+                # Use AudioData to convert the raw data to wav data.
+                audio_data = sr.AudioData(last_sample, source.SAMPLE_RATE, source.SAMPLE_WIDTH)
+                wav_data = io.BytesIO(audio_data.get_wav_data())
+
+                # Write wav data to the temporary file as bytes.
+                with open(temp_file, 'w+b') as f:
+                    f.write(wav_data.read())
+
+                # Read the transcription.
+                text = pipe(temp_file, chunk_length_s=30, return_timestamps=False, generate_kwargs={"language": language, "task": task})["text"]
+                
+
+                # If we detected a pause between recordings, add a new item to our transcription.
+                # Otherwise edit the existing one.
+                if phrase_complete:
+                    transcription.append(text)
+                else:
+                    transcription[-1] = text
+
+                # Clear the console to reprint the updated transcription.
+                os.system('cls' if os.name == 'nt' else 'clear')
+                for line in transcription:
+                    print(line)
+                # Flush stdout.
+                print('', end='', flush=True)
+
+                # Infinite loops are bad for processors, must sleep.
+                sleep(0.25)
+        except KeyboardInterrupt:
+            break
+
+    print("\n\nTranscription:")
+    for line in transcription:
+        print(line)
+
+if __name__ == "__main__":
+    main()