def test_synthesize():
model_path = os.path.join("files", "tacotron2_statedict.pt")
waveglow_path = os.path.join("files",
"waveglow_256channels_universal_v5.pt")
graph_path = "graph.png"
audio_path = "synthesized_audio.wav"
model = load_model(model_path)
assert model
waveglow = load_waveglow_model(waveglow_path)
assert waveglow
text = "hello everybody my name is david attenborough"
inflect_engine = inflect.engine()
synthesize(model,
waveglow,
text,
inflect_engine,
graph=graph_path,
audio=audio_path)
assert text_similarity(text, transcribe(audio_path)) > 0.5
assert os.path.isfile(graph_path)
assert os.path.isfile(audio_path)
os.remove(graph_path)
os.remove(audio_path)
def process_segments(audio_path,
output_path,
segments,
min_length,
max_length,
logging=logging):
logging.info("Generating segments...")
samples = []
total = len(segments)
index = 0
for i in range(total):
segment = segments[i]
_, time_start, time_end = segment
time_length = time_end - time_start
if time_length >= min_length and time_length <= max_length:
name = cut_audio(audio_path, int(time_start), int(time_end),
output_path)
clip_path = os.path.join(output_path, name)
transcript = transcribe(clip_path)
if transcript:
samples.append({
"index": index,
"start": time_start,
"end": time_end,
"name": name,
"transcript": transcript.strip(),
})
index += 1
logging.info(f"Progress - {i+1}/{total}")
return samples
def transcribe_clips(folder, labels, output_path):
files = os.listdir(folder)[:5]
labels = read_labels(labels)
data = []
for filename in tqdm(files):
prediction = transcribe(os.path.join(folder, filename))
actual = labels[filename[:-4]]
score = compare(prediction, actual)
data.append(Transcription(filename, prediction, actual, score))
save_results(data, output_path)
def evalulate_audio(audio, text):
"""
Gets list of words not recognised in the audio.
Compares the transcription and given text.
Parameters
----------
audio : str
Path to audio file
text : str
Synthesised text
Returns
-------
set
Set of words not recognised in the audio
"""
results = transcribe(audio)
original_words = text.split(" ")
produced_words = results.split(" ")
return set(original_words) - set(produced_words)
def process_segments(audio_path,
output_path,
segments,
min_length,
max_length,
logging=logging):
"""
Generates audio clips and reduces segments to only valid ones.
This includes removing segements which are too long, too short or cannot be transcribed.
Parameters
----------
audio_path : str
Path to audio file
output_path : str
Path to save clips to
segments : list
List of segments produced in get_segments
min_length : int
Minimum length of a clip (in milliseconds)
max_length : int
Maximum length of a clip (in milliseconds)
logging : logging (optional)
Logging object to write progress to
Returns
-------
list
List of samples (dictionaries containing clip index, start, end, name & transcript)
"""
logging.info("Generating segments...")
samples = []
total = len(segments)
index = 0
for i in range(total):
segment = segments[i]
_, time_start, time_end = segment
time_length = time_end - time_start
if time_length >= min_length and time_length <= max_length:
name = cut_audio(audio_path, int(time_start), int(time_end),
output_path)
clip_path = os.path.join(output_path, name)
try:
transcript = transcribe(clip_path)
except:
logging.info(f"Could not transcribe {clip_path}")
transcript = None
if transcript:
samples.append({
"index": index,
"start": time_start,
"end": time_end,
"name": name,
"transcript": transcript.strip(),
})
index += 1
logging.info(f"Progress - {i+1}/{total}")
return samples
def evalulate_audio(audio, text):
results = transcribe(audio)
original_words = text.split(" ")
produced_words = results.split(" ")
return set(original_words) - set(produced_words)