Proj Refactor + New libs (#29)

- i'm especially proud of the new huggingface & langchain code
- i need to create better usage-guidance documentation throughout this library

HuggingFace/audio_transcription/MicrophoneTranscription/requirements.txt

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+pyaudio
+numpy
+torch
+transformers

HuggingFace/audio_transcription/MicrophoneTranscription/run.py

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+import argparse
+import logging
+import sys
+
+from transcribe_microphone import RealTimeASR
+
+
+def main(args):
+    # Initialize the RealTimeASR object
+    asr_app = RealTimeASR(maxlen=args.maxlen)
+    asr_app.initialize_audio()
+
+    # Set up logging
+    try:
+        with open(args.log_file, "a") as f:
+            logging.basicConfig(filename=args.log_file, level=logging.INFO)
+    except (FileNotFoundError, PermissionError):
+        print(f"Error opening log file: {args.log_file}", file=sys.stderr, flush=True)
+        args.log_file = None
+
+    try:
+        # Capture and transcribe audio in real-time
+        if asr_app.stream.is_active() and asr_app.asr_pipeline.is_running():
+            asr_app.capture_and_transcribe(log_file=args.log_file)
+        else:
+            print("Error: PyAudio stream or ASR pipeline is not active.", file=sys.stderr, flush=True)
+    except KeyboardInterrupt:
+        logging.info("Transcription stopped by user.")
+        print("Stopping transcription.")
+    except Exception as e:
+        logging.exception("Error during transcription.")
+        print(f"Error during transcription: {e}", file=sys.stderr, flush=True)
+    finally:
+        # Close the PyAudio stream and write final transcriptions to the log file
+        asr_app.close_stream(log_file=args.log_file)
+
+
+if __name__ == "__main__":
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser(description="Real-time ASR using the Transformers library.")
+    parser.add_argument("--maxlen", type=int, default=300, help="Maximum number of transcriptions to store in the cache.")
+    parser.add_argument("--log-file", type=str, default="transcription_log.txt", help="Path to the log file to write transcriptions to.")
+    args = parser.parse_args()
+
+    main(args)

HuggingFace/audio_transcription/MicrophoneTranscription/transcribe_microphone.py

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+"""
+Long-Form Transcription
+The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers pipeline method. Chunking is enabled by setting chunk_length_s=30 when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing return_timestamps=True:
+"""
+
+import pyaudio
+import numpy as np
+import torch
+from transformers import pipeline
+from collections import deque
+import sys
+import os
+
+
+class RealTimeASR:
+    """
+    This class demonstrates how to perform real-time ASR using the pipeline method of the Transformers library.
+    """
+    def __init__(self, maxlen=300):
+        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+        self.asr_pipeline = pipeline(
+            "automatic-speech-recognition",
+            model="openai/whisper-large-v2",
+            chunk_length_s=30,
+            device=self.device,
+            return_timestamps=True
+        )
+        self.transcription_cache = deque(maxlen=maxlen)
+        self.sliding_window = np.array([])
+
+    def initialize_audio(self):
+        self.p = pyaudio.PyAudio()
+        self.stream = self.p.open(format=pyaudio.paInt16,
+                                  channels=1,
+                                  rate=16000,
+                                  input=True,
+                                  frames_per_buffer=1024)
+
+    def capture_and_transcribe(self, log_file=None):
+        """
+        Continuously captures audio from the microphone, concatenates it to a sliding window, and transcribes the audio
+        using the ASR pipeline. If the sliding window is longer than 30 seconds, the pipeline is run on the first 30 seconds
+        of audio and the sliding window is shifted by 5 seconds. If there is a transcription in the cache, it is printed to
+        stdout and written to the log file.
+
+        Args:
+            log_file (str): The path to the log file to write transcriptions to. If None, transcriptions will not be written
+            to a log file.
+
+        Returns:
+            None
+        """
+        # Check if the log file path is valid before writing to it
+        if log_file is not None:
+            try:
+                with open(log_file, "a") as f:
+                    pass
+            except (FileNotFoundError, PermissionError):
+                print(f"Error opening log file: {log_file}", file=sys.stderr, flush=True)
+                log_file = None
+
+        while True:
+            # Check if the PyAudio stream is active before attempting to read from it
+            if self.stream.is_active():
+                # Capture audio from the microphone
+                audio_data = np.frombuffer(self.stream.read(1024), dtype=np.int16)
+
+                # Concatenate the audio data to the sliding window
+                self.sliding_window = np.concatenate((self.sliding_window, audio_data))
+
+                # Check if the sliding window is shorter than the ASR pipeline chunk length before attempting to transcribe it
+                if len(self.sliding_window) >= 16000 * self.asr_pipeline.task.config.chunk_size_ms / 1000:
+                    # Check if the sliding window is shorter than 30 seconds before attempting to transcribe it
+                    if len(self.sliding_window) < 16000 * 30:
+                        # Transcribe the sliding window and shift it by the shift length
+                        transcription = self.asr_pipeline(self.sliding_window)
+                        # Check if the ASR pipeline returns a transcription before appending it to the cache
+                        if "text" in transcription:
+                            # Check if the ASR pipeline returns timestamps before appending them to the cache
+                            if "timestamps" in transcription:
+                                # Check if the transcription cache is full before attempting to append a new transcription
+                                if len(self.transcription_cache) == self.transcription_cache.maxlen:
+                                    self.transcription_cache.popleft()
+                                self.transcription_cache.append(transcription)
+                                self.sliding_window = np.array([])
+                            else:
+                                print("Error: ASR pipeline does not return timestamps.", file=sys.stderr, flush=True)
+                        else:
+                            print("Error transcribing audio.", file=sys.stderr, flush=True)
+                    else:
+                        # Transcribe the first 30 seconds of audio and shift the sliding window by the shift length
+                        transcription = self.asr_pipeline(self.sliding_window[:16000 * 30])
+                        # Check if the ASR pipeline returns a transcription before appending it to the cache
+                        if "text" in transcription:
+                            # Check if the ASR pipeline returns timestamps before appending them to the cache
+                            if "timestamps" in transcription:
+                                # Check if the transcription cache is full before attempting to append a new transcription
+                                if len(self.transcription_cache) == self.transcription_cache.maxlen:
+                                    self.transcription_cache.popleft()
+                                self.transcription_cache.append(transcription)
+                                self.sliding_window = self.sliding_window[16000 * self.asr_pipeline.task.config.shift_ms / 1000:]
+                            else:
+                                print("Error: ASR pipeline does not return timestamps.", file=sys.stderr, flush=True)
+                        else:
+                            print("Error transcribing audio.", file=sys.stderr, flush=True)
+
+                # If there is a transcription in the cache, print it to stdout and write it to the log file
+                if len(self.transcription_cache) > 0:
+                    transcription = self.transcription_cache.popleft()
+                    # Check if the transcription cache is empty before attempting to pop a transcription
+                    if transcription is not None:
+                        # Check if the ASR pipeline returns timestamps before appending them to the cache
+                        if "timestamps" in transcription:
+                            print(transcription["text"], file=sys.stdout, flush=True)
+                            if log_file is not None:
+                                # Check if the log file is a file before writing to it
+                                if not os.path.isfile(log_file):
+                                    print(f"Error writing to log file: {log_file}", file=sys.stderr, flush=True)
+                                else:
+                                    # Check if the log file directory exists before writing to it
+                                    log_dir = os.path.dirname(log_file)
+                                    if not os.path.isdir(log_dir):
+                                        print(f"Error writing to log file: {log_file}", file=sys.stderr, flush=True)
+                                    else:
+                                        # Check if the user has permission to write to the log file before writing to it
+                                        if not os.access(log_file, os.W_OK):
+                                            print(f"Error writing to log file: {log_file}", file=sys.stderr, flush=True)
+                                        else:
+                                            # Check if the log file is too large before writing to it
+                                            if os.path.isfile(log_file) and os.path.getsize(log_file) > 1000000:
+                                                log_file = create_new_log_file(log_file)
+                                            try:
+                                                with open(log_file, "a") as f:
+                                                    f.write(transcription["text"] + "\n")
+                                            except (FileNotFoundError, PermissionError):
+                                                print(f"Error writing to log file: {log_file}", file=sys.stderr, flush=True)
+                        else:
+                            print("Error: ASR pipeline does not return timestamps.", file=sys.stderr, flush=True)
+            else:
+                # Check if the PyAudio stream is stopped before closing it
+                if self.stream.is_stopped():
+                    self.stream.close()
+                    # Check if the PyAudio library is terminated before closing the stream
+                    if self.p.is_terminated():
+                        self.p.terminate()
+                        # Write the final transcriptions to the log file
+                        if log_file is not None:
+                            # Check if the log file is writable before writing to it
+                            if not os.access(log_file, os.W_OK):
+                                print(f"Error writing to log file: {log_file}", file=sys.stderr, flush=True)
+                            else:
+                                try:
+                                    with open(log_file, "a") as f:
+                                        for transcription in self.transcription_cache:
+                                            if transcription is not None:
+                                                # Check if the ASR pipeline returns timestamps before appending them to the cache
+                                                if "timestamps" in transcription:
+                                                    # Check if the transcription cache is full before attempting to append a new transcription
+                                                    if len(self.transcription_cache) == self.transcription_cache.maxlen:
+                                                        self.transcription_cache.popleft()
+                                                    self.transcription_cache.append(transcription)
+                                                    f.write(transcription["text"] + "\n")
+                                                else:
+                                                    print("Error: ASR pipeline does not return timestamps.", file=sys.stderr, flush=True)
+                                except (FileNotFoundError, PermissionError):
+                                    print(f"Error writing to log file: {log_file}", file=sys.stderr, flush=True)
+                    else:
+                        print("Error terminating PyAudio library.", file=sys.stderr, flush=True)
+                else:
+                    print("Error stopping PyAudio stream.", file=sys.stderr, flush=True)
+                break
+
+
+    def close_stream(self, log_file=None):
+        self.stream.stop_stream()
+        self.stream.close()
+        self.p.terminate()
+
+        # Write the final transcription to the log file
+        if log_file is not None:
+            with open(log_file, "a") as f:
+                for transcription in self.transcription_cache:
+                    f.write(transcription + "\n")
+
+
+def create_new_log_file(log_file):
+    """
+    Creates a new log file with a different name if the original log file is too large.
+
+    Args:
+        log_file (str): The path to the original log file.
+
+    Returns:
+        str: The path to the new log file.
+    """
+    log_dir = os.path.dirname(log_file)
+    log_name, log_ext = os.path.splitext(os.path.basename(log_file))
+    i = 1
+    while True:
+        new_log_name = f"{log_name}_{i}{log_ext}"
+        new_log_file = os.path.join(log_dir, new_log_name)
+        if not os.path.isfile(new_log_file):
+            return new_log_file
+        i += 1

HuggingFace/audio_transcription/test/test_at1.py

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+import unittest
+import numpy as np
+from io import StringIO
+from contextlib import redirect_stdout
+from ..MicrophoneTranscription.transcribe_microphone import RealTimeASR
+
+
+import unittest
+import numpy as np
+from io import StringIO
+from contextlib import redirect_stdout
+from RealTimeASR import RealTimeASR
+
+class TestRealTimeASR(unittest.TestCase):
+    """
+    This class contains unit tests for the RealTimeASR class.
+    """
+    def setUp(self):
+        """
+        This method sets up the test environment before each test case is run.
+        """
+        self.asr_app = RealTimeASR()
+        self.asr_app.initialize_audio()
+
+    def test_sliding_window(self):
+        """
+        This method tests that the sliding window is correctly updated with new audio data.
+        """
+        audio_data = np.ones(16000, dtype=np.int16)
+        self.asr_app.sliding_window = np.array([])
+        self.asr_app.sliding_window = np.concatenate((self.asr_app.sliding_window, audio_data))
+        self.assertEqual(len(self.asr_app.sliding_window), 16000)
+
+    def test_transcription_cache(self):
+        """
+        This method tests that the transcription cache is correctly updated with new transcriptions.
+        """
+        transcription = {"text": "hello world"}
+        self.asr_app.transcription_cache.append(transcription["text"])
+        self.assertEqual(len(self.asr_app.transcription_cache), 1)
+        self.assertEqual(self.asr_app.transcription_cache[0], "hello world")
+
+    def test_capture_and_transcribe(self):
+        """
+        This method tests that the capture_and_transcribe method correctly transcribes audio.
+        """
+        audio_data = np.ones(16000 * 30, dtype=np.int16)
+        self.asr_app.sliding_window = np.array([])
+        self.asr_app.sliding_window = np.concatenate((self.asr_app.sliding_window, audio_data))
+        with redirect_stdout(StringIO()):
+            self.asr_app.capture_and_transcribe()
+        self.assertEqual(len(self.asr_app.transcription_cache), 1)
+        self.assertTrue(isinstance(self.asr_app.transcription_cache[0], str))
+
+    def test_close_stream(self):
+        """
+        This method tests that the stream is closed correctly.
+        """
+        self.asr_app.close_stream()
+        self.assertTrue(self.asr_app.stream.is_stopped())
+        self.assertTrue(self.asr_app.stream.is_closed())
+
+    def test_device(self):
+        """
+        This method tests that the device is correctly set.
+        """
+        self.assertTrue(self.asr_app.device in ["cuda:0", "cpu"])
+
+    def test_asr_pipeline(self):
+        """
+        This method tests that the ASR pipeline is correctly set.
+        """
+        self.assertTrue(isinstance(self.asr_app.asr_pipeline, RealTimeASR))
+
+    def test_sliding_window_shift(self):
+        """
+        This method tests that the sliding window is correctly shifted.
+        """
+        audio_data = np.ones(16000 * 30, dtype=np.int16)
+        self.asr_app.sliding_window = np.array([])
+        self.asr_app.sliding_window = np.concatenate((self.asr_app.sliding_window, audio_data))
+        with redirect_stdout(StringIO()):
+            self.asr_app.capture_and_transcribe()
+        self.assertEqual(len(self.asr_app.transcription_cache), 1)
+        self.assertTrue(isinstance(self.asr_app.transcription_cache[0], str))
+        self.assertEqual(len(self.asr_app.sliding_window), 16000 * 5)
+
+    def tearDown(self):
+        """
+        This method tears down the test environment after each test case is run.
+        """
+        self.asr_app.close_stream()
+
+
+if __name__ == '__main__':
+    unittest.main()