def harvest(cmd):
docid = cmd["id"]
meta = rec_set.get_meta(docid)
x, fs = librosa.load(os.path.join(get_attachpath(), meta["path"]), sr=None)
print("SYSTEM: harvesting...")
hv_start = time.time()
f0, timeaxis = pyworld.harvest(x.astype(np.float64), fs)
print(f"SYSTEM: finished harvesting! (took {time.time() - hv_start:.2f}s)")
with tempfile.NamedTemporaryFile(suffix=".txt", delete=False,
mode="w") as harvest_fp:
for i in range(len(timeaxis)):
harvest_fp.write(f'{timeaxis[i]} {f0[i]}\n')
if len(open(harvest_fp.name).read().strip()) == 0:
return {"error": "Harvest computation failed"}
# XXX: frozen attachdir
harvesthash = guts.attach(harvest_fp.name, get_attachpath())
guts.bschange(
rec_set.dbs[docid],
{
"type": "set",
"id": "meta",
"key": "harvest",
"val": harvesthash
},
)
return {"harvest": harvesthash}
def pitch(cmd):
docid = cmd["id"]
meta = rec_set.get_meta(docid)
# Create an 8khz wav file
with tempfile.NamedTemporaryFile(suffix=".wav") as wav_fp:
ff_start = time.time()
subprocess.call([
"ffmpeg",
"-y",
"-loglevel",
"panic",
"-i",
os.path.join(get_attachpath(), meta["path"]),
"-ar",
"8000",
"-ac",
"1",
wav_fp.name,
])
print(f'SYSTEM: FFMPEG took {time.time() - ff_start:.2f}s')
# ...and use it to compute pitch
with tempfile.NamedTemporaryFile(suffix=".txt",
delete=False) as pitch_fp:
subprocess.call([get_calc_sbpca(), wav_fp.name, pitch_fp.name])
if len(open(pitch_fp.name).read().strip()) == 0:
return {"error": "Pitch computation failed"}
# XXX: frozen attachdir
pitchhash = guts.attach(pitch_fp.name, get_attachpath())
guts.bschange(
rec_set.dbs[docid],
{
"type": "set",
"id": "meta",
"key": "pitch",
"val": pitchhash
},
)
return {"pitch": pitchhash}
def gen_mat(cmd):
id = cmd["id"]
# Hm!
meta = rec_set.get_meta(id)
out = {}
measure = _measure(id, raw=True)
out.update(measure["measure"])
# out.update(measure["raw"])
if meta.get("rms"):
out["rms"] = np.array(
json.load(open(os.path.join(get_attachpath(), meta["rms"]))))
if meta.get("pitch"):
p_path = os.path.join(get_attachpath(), meta["pitch"])
out["pitch"] = np.array(
[float(X.split()[1]) for X in open(p_path) if len(X.split()) > 2])
if meta.get("align"):
a_path = os.path.join(get_attachpath(), meta["align"])
out["align"] = json.load(open(a_path))
# Remove 'None' values
for seg in out['align']['segments']:
for k, v in list(seg.items()):
if v is None:
del seg[k]
with tempfile.NamedTemporaryFile(suffix=".mat", delete=False) as mf:
sio.savemat(mf.name, out)
mathash = guts.attach(mf.name, get_attachpath())
guts.bschange(rec_set.dbs[id], {
"type": "set",
"id": "meta",
"key": "mat",
"val": mathash
})
return {"mat": mathash}
def rms(cmd):
docid = cmd["id"]
info = rec_set.get_meta(docid)
vpath = os.path.join(get_attachpath(), info["path"])
R = 44100
snd = nmt.sound2np(vpath,
R=R,
nchannels=1,
ffopts=["-filter:a", "dynaudnorm"])
WIN_LEN = int(R / 100)
rms = []
for idx in range(int(len(snd) / WIN_LEN)):
chunk = snd[idx * WIN_LEN:(idx + 1) * WIN_LEN]
rms.append((chunk.astype(float)**2).sum() / len(chunk))
rms = np.array(rms)
rms -= rms.min()
rms /= rms.max()
with tempfile.NamedTemporaryFile(suffix=".json", delete=False,
mode="w") as fh:
json.dump(rms.tolist(), fh)
fh.close()
rmshash = guts.attach(fh.name, get_attachpath())
guts.bschange(rec_set.dbs[docid], {
"type": "set",
"id": "meta",
"key": "rms",
"val": rmshash
})
return {"rms": rmshash}
def _measure(id=None,
start_time=None,
end_time=None,
full_ts=False,
force_gen=False,
raw=False):
if start_time is not None:
start_time = float(start_time)
if end_time is not None:
end_time = float(end_time)
meta = rec_set.get_meta(id)
# full transcription duration should be the same for any given document,
# prosodic measures for these are cached so we can bulk download them.
if full_ts and not force_gen and meta.get("full_ts"):
cached = json.load(
open(os.path.join(get_attachpath(), meta["full_ts"])))
# if dynamism is part of cached data, return it. otherwise, it is outdated and must be reloaded
if 'Dynamism' in cached or not calc_intense:
return cached
pitch = [[float(Y) for Y in X.split(" ")]
for X in open(os.path.join(get_attachpath(), meta["pitch"]))]
# redundacy, CSV did not load sometimes on older versions of Drift. Generate if nonexistent
if not meta.get("csv"):
gen_csv({"id": id})
# if not meta.get("info"):
# save_audio_info({ "id": id })
# check, maybe Drift is now running on calc_intense mode even though it wasn't when the audio file was originally uploaded
# Generate Harvest if nonexistent
if calc_intense and not meta.get("harvest"):
harvest({"id": id})
# TODO will these hang? this is just to prevent concurrent calls to harvest/csv during their initialization throwing errors
while not rec_set.get_meta(id).get("csv"):
pass
# while not rec_set.get_meta(id).get("info"):
# pass
while calc_intense and not rec_set.get_meta(id).get("harvest"):
pass
meta = rec_set.get_meta(id)
driftcsv = open(os.path.join(get_attachpath(), meta["csv"]))
gentlecsv = open(os.path.join(get_attachpath(), meta["aligncsv"]))
st = start_time if start_time is not None else 0
full_data = {
"measure": {
"start_time": st,
"end_time": end_time if end_time is not None else
(len(pitch) / 100.0)
}
}
gentle_drift_data = prosodic_measures.measure_gentle_drift(
gentlecsv, driftcsv, start_time, end_time)
full_data["measure"].update(gentle_drift_data)
if calc_intense:
voxit_data = prosodic_measures.measure_voxit(
os.path.join(get_attachpath(), meta["path"]),
open(os.path.join(get_attachpath(), meta["pitch"])),
open(os.path.join(get_attachpath(), meta["harvest"])), start_time,
end_time)
full_data["measure"].update(voxit_data)
if full_ts:
with tempfile.NamedTemporaryFile(suffix=".json",
delete=False,
mode="w") as dfh:
json.dump(full_data, dfh, indent=2)
dfh.close()
fulltshash = guts.attach(dfh.name, get_attachpath())
guts.bschange(
rec_set.dbs[id],
{
"type": "set",
"id": "meta",
"key": "full_ts",
"val": fulltshash
},
)
return full_data
def gen_csv(cmd):
docid = cmd["id"]
meta = rec_set.get_meta(docid)
p_path = os.path.join(get_attachpath(), meta["pitch"])
pitch = [float(X.split()[1]) for X in open(p_path) if len(X.split()) > 2]
a_path = os.path.join(get_attachpath(), meta["align"])
align = json.load(open(a_path))
words = []
for seg in align["segments"]:
for wd in seg["wdlist"]:
wd_p = dict(wd)
wd_p["speaker"] = seg["speaker"]
words.append(wd_p)
with tempfile.NamedTemporaryFile(suffix=".csv", delete=False,
mode="w") as fp:
w = csv.writer(fp)
w.writerow(["time (s)", "pitch (hz)", "word", "phoneme", "speaker"])
for idx, pitch_val in enumerate(pitch):
t = idx / 100.0
wd_txt = None
ph_txt = None
speaker = None
for wd_idx, wd in enumerate(words):
if wd.get("start") is None or wd.get("end") is None:
continue
if wd["start"] <= t and wd["end"] >= t:
wd_txt = wd["word"].encode("utf-8")
speaker = wd["speaker"]
# find phone
cur_t = wd["start"]
for phone in wd.get("phones", []):
if cur_t + phone["duration"] >= t:
ph_txt = phone["phone"]
break
cur_t += phone["duration"]
break
if type(wd_txt) == bytes:
wd_txt = wd_txt.decode("utf-8")
elif type(wd_txt) != str:
wd_txt = str(wd_txt or "")
row = [t, pitch_val, wd_txt, ph_txt, speaker]
w.writerow(row)
fp.flush()
csvhash = guts.attach(fp.name, get_attachpath())
guts.bschange(
rec_set.dbs[cmd["id"]],
{
"type": "set",
"id": "meta",
"key": "csv",
"val": csvhash
},
)
return {"csv": csvhash}
def align(cmd):
meta = rec_set.get_meta(cmd["id"])
media = os.path.join(get_attachpath(), meta["path"])
segs = parse_speakers_in_transcript(
open(os.path.join(get_attachpath(), meta["transcript"])).read())
tscript_txt = "\n".join([X["line"] for X in segs])
url = f"http://localhost:{GENTLE_PORT}/transcriptions"
res = requests.post(url,
data={"transcript": tscript_txt},
files={'audio': ('audio', open(media, 'rb'))})
# Find the ID
uid = res.history[0].headers['Location'].split('/')[-1]
# Poll for status
status_url = url + '/' + uid + '/status.json'
cur_status = -1
while True:
status = requests.get(status_url).json()
if status.get('status') != 'OK':
s = status.get('percent', 0)
if s > cur_status:
cur_status = s
guts.bschange(
rec_set.dbs[cmd["id"]], {
"type": "set",
"id": "meta",
"key": "align_px",
"val": cur_status
})
time.sleep(1)
else:
# transcription done
break
align_url = url + '/' + uid + '/align.json'
trans = requests.get(align_url).json()
# Re-diarize Gentle output into a sane diarization format
diary = {"segments": [{}]}
seg = diary["segments"][0]
seg["speaker"] = segs[0]["speaker"]
wdlist = []
end_offset = 0
seg_idx = 0
cur_end = 0
for wd in trans["words"]:
gap = trans["transcript"][end_offset:wd["startOffset"]]
seg_idx += len(gap.split("\n")) - 1
if "\n" in gap and len(wdlist) > 0:
# Linebreak - new segment!
wdlist[-1]["word"] += gap.split("\n")[0]
seg["wdlist"] = gentle_punctuate(wdlist, trans["transcript"])
# Compute start & end
seg["start"] = seg["wdlist"][0].get("start", cur_end)
has_end = [X for X in seg["wdlist"] if X.get("end")]
if len(has_end) > 0:
seg["end"] = has_end[-1]["end"]
else:
seg["end"] = cur_end
cur_end = seg["end"]
wdlist = []
seg = {}
diary["segments"].append(seg)
if len(segs) > seg_idx:
seg["speaker"] = segs[seg_idx]["speaker"]
wdlist.append(wd)
end_offset = wd["endOffset"]
seg["wdlist"] = gentle_punctuate(wdlist, trans["transcript"])
# Compute start & end
seg["start"] = seg["wdlist"][0].get("start", cur_end)
has_end = [X for X in seg["wdlist"] if X.get("end")]
if len(has_end) > 0:
seg["end"] = has_end[-1]["end"]
else:
seg["end"] = cur_end
# For now, hit disk. Later we can explore the transcription DB.
with tempfile.NamedTemporaryFile(suffix=".json", delete=False,
mode="w") as dfh:
json.dump(diary, dfh, indent=2)
dfh.close()
alignhash = guts.attach(dfh.name, get_attachpath())
guts.bschange(
rec_set.dbs[cmd["id"]],
{
"type": "set",
"id": "meta",
"key": "align",
"val": alignhash
},
)
# https://stackoverflow.com/questions/45978295/saving-a-downloaded-csv-file-using-python
with tempfile.NamedTemporaryFile(suffix=".csv", delete=False,
mode="w") as fp:
w = csv.writer(fp)
aligncsv_url = url + '/' + uid + '/align.csv'
aligncsv = requests.get(aligncsv_url)
for line in aligncsv.iter_lines():
w.writerow(line.decode('utf-8').split(','))
fp.close()
aligncsvhash = guts.attach(fp.name, get_attachpath())
guts.bschange(
rec_set.dbs[cmd["id"]],
{
"type": "set",
"id": "meta",
"key": "aligncsv",
"val": aligncsvhash
},
)
return {"align": alignhash}