add speech recognition tutorial with transformers

Author: x4nth055

README.md

@@ -46,6 +46,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [Conversational AI Chatbot with Transformers in Python](https://www.thepythoncode.com/article/conversational-ai-chatbot-with-huggingface-transformers-in-python). ([code](machine-learning/nlp/chatbot-transformers))
         - [How to Pretrain BERT using Transformers in Python](https://www.thepythoncode.com/article/pretraining-bert-huggingface-transformers-in-python). ([code](machine-learning/nlp/pretraining-bert))
         - [How to Perform Machine Translation using Transformers in Python](https://www.thepythoncode.com/article/machine-translation-using-huggingface-transformers-in-python). ([code](machine-learning/nlp/machine-translation))
+        - [Speech Recognition using Transformers in Python](https://www.thepythoncode.com/article/speech-recognition-using-huggingface-transformers-in-python). ([code](machine-learning/nlp/speech-recognition-transformers))
     - ### [Computer Vision](https://www.thepythoncode.com/topic/computer-vision)
         - [How to Detect Human Faces in Python using OpenCV](https://www.thepythoncode.com/article/detect-faces-opencv-python). ([code](machine-learning/face_detection))
         - [How to Make an Image Classifier in Python using TensorFlow and Keras](https://www.thepythoncode.com/article/image-classification-keras-python). ([code](machine-learning/image-classifier))

machine-learning/nlp/speech-recognition-transformers/16-122828-0002.wav

@@ -0,0 +1,143 @@
+# %%
+# !pip install transformers==4.11.2 datasets soundfile sentencepiece torchaudio pyaudio
+
+# %%
+from transformers import *
+import torch
+import soundfile as sf
+# import librosa
+import os
+import torchaudio
+
+# %%
+# model_name = "facebook/wav2vec2-base-960h" # 360MB
+model_name = "facebook/wav2vec2-large-960h-lv60-self" # 1.18GB
+
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+
+# %%
+# audio_url = "http://www.fit.vutbr.cz/~motlicek/sympatex/f2bjrop1.0.wav"
+# audio_url = "http://www.fit.vutbr.cz/~motlicek/sympatex/f2bjrop1.1.wav"
+# audio_url = "http://www.fit.vutbr.cz/~motlicek/sympatex/f2btrop6.0.wav"
+# audio_url = "https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/16-122828-0002.wav"
+audio_url = "https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/30-4447-0004.wav"
+# audio_url = "https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/7601-291468-0006.wav"
+# audio_url = "https://file-examples-com.github.io/uploads/2017/11/file_example_WAV_1MG.wav"
+# audio_url = "http://www0.cs.ucl.ac.uk/teaching/GZ05/samples/lathe.wav"
+
+# %%
+# load our wav file
+speech, sr = torchaudio.load(audio_url)
+speech = speech.squeeze()
+# or using librosa
+# speech, sr = librosa.load(audio_file, sr=16000)
+sr, speech.shape
+
+# %%
+# resample from whatever the audio sampling rate to 16000
+resampler = torchaudio.transforms.Resample(sr, 16000)
+speech = resampler(speech)
+speech.shape
+
+# %%
+# tokenize our wav
+input_values = processor(speech, return_tensors="pt", sampling_rate=16000)["input_values"]
+input_values.shape
+
+# %%
+# perform inference
+logits = model(input_values)["logits"]
+logits.shape
+
+# %%
+# use argmax to get the predicted IDs
+predicted_ids = torch.argmax(logits, dim=-1)
+predicted_ids.shape
+
+# %%
+# decode the IDs to text
+transcription = processor.decode(predicted_ids[0])
+transcription.lower()
+
+# %%
+def get_transcription(audio_path):
+  # load our wav file
+  speech, sr = torchaudio.load(audio_path)
+  speech = speech.squeeze()
+  # or using librosa
+  # speech, sr = librosa.load(audio_file, sr=16000)
+  # resample from whatever the audio sampling rate to 16000
+  resampler = torchaudio.transforms.Resample(sr, 16000)
+  speech = resampler(speech)
+  # tokenize our wav
+  input_values = processor(speech, return_tensors="pt", sampling_rate=16000)["input_values"]
+  # perform inference
+  logits = model(input_values)["logits"]
+  # use argmax to get the predicted IDs
+  predicted_ids = torch.argmax(logits, dim=-1)
+  # decode the IDs to text
+  transcription = processor.decode(predicted_ids[0])
+  return transcription.lower()
+
+# %%
+get_transcription(audio_url)
+
+# %%
+import pyaudio
+import wave
+
+# the file name output you want to record into
+filename = "recorded.wav"
+# set the chunk size of 1024 samples
+chunk = 1024
+# sample format
+FORMAT = pyaudio.paInt16
+# mono, change to 2 if you want stereo
+channels = 1
+# 44100 samples per second
+sample_rate = 16000
+record_seconds = 10
+# initialize PyAudio object
+p = pyaudio.PyAudio()
+# open stream object as input & output
+stream = p.open(format=FORMAT,
+                channels=channels,
+                rate=sample_rate,
+                input=True,
+                output=True,
+                frames_per_buffer=chunk)
+frames = []
+print("Recording...")
+for i in range(int(sample_rate / chunk * record_seconds)):
+    data = stream.read(chunk)
+    # if you want to hear your voice while recording
+    # stream.write(data)
+    frames.append(data)
+print("Finished recording.")
+# stop and close stream
+stream.stop_stream()
+stream.close()
+# terminate pyaudio object
+p.terminate()
+# save audio file
+# open the file in 'write bytes' mode
+wf = wave.open(filename, "wb")
+# set the channels
+wf.setnchannels(channels)
+# set the sample format
+wf.setsampwidth(p.get_sample_size(FORMAT))
+# set the sample rate
+wf.setframerate(sample_rate)
+# write the frames as bytes
+wf.writeframes(b"".join(frames))
+# close the file
+wf.close()
+
+# %%
+get_transcription("recorded.wav")
+
+# %%
+
+
+

machine-learning/nlp/speech-recognition-transformers/30-4447-0004.wav

@@ -0,0 +1,5 @@
+# [Speech Recognition using Transformers in Python](https://www.thepythoncode.com/article/speech-recognition-using-huggingface-transformers-in-python)
+To get it running:
+- `pip3 install -r requirements.txt`
+
+Check the [the tutorial](https://www.thepythoncode.com/article/speech-recognition-using-huggingface-transformers-in-python) and the [Colab notebook](https://colab.research.google.com/drive/1-0M8zvQrOzlZ8U8l7KdPOuLBNtzqtlsz?usp=sharing) for more information.

machine-learning/nlp/speech-recognition-transformers/7601-291468-0006.wav

@@ -0,0 +1,5 @@
+transformers==4.11.2
+soundfile
+sentencepiece
+torchaudio
+pyaudio